Estimationof!sexfromthe! morphometric!assessment!of!hand … · Estimationof!sexfromthe!...

Estimation of sex from the

morphometric assessment of hand

bones in a Western Australian

population

Rebecca DeSilva (BSc, GDipForSci)

Centre for Forensic Science

University of Western Australia

This thesis is presented for the degree of

Master of Forensic Science

i

DECLARATION

I declare that the research presented in this thesis, for the Master of Forensic

Science at the University of Western Australia, is my own work. The results of the

work have not been submitted for assessment, in full or part, within any other

tertiary institute, except where due acknowledgement has been made in the text.

………………………………………………

Rebecca DeSilva

ABSTRACT

iii

ABSTRACT

The role of a forensic anthropologist in medico-‐legal investigations is to, ultimately

contribute toward establishing the identity of unknown skeletal remains by

constructing a biological profile; which involves estimations of sex, age, stature

and ancestry. Recent research relating to the formulation of sex estimation

standards has focussed on the morphological assessment of bones other than the

pelvis and cranium, such as the long bones of the appendicular skeleton. In

particular, sex estimation standards based on morphometric data from the

metacarpals and phalanges have reported classification accuracy rates of 80% and

above. As it has been established that the application of foreign skeletal standards

can result in the misclassification of sex, the purpose of this study is to produce

population specific sex estimation standards for a contemporary Western

Australian population. The age at which hand bones are metrically sexually

dimorphic is also examined to determine the minimum age at which sex can be

reliably estimated.

The present study examines digital right hand x-‐rays of 300 adults and 100 sub-‐

adults, equally represented by sex. A total of 40 measurements are taken in the

metacarpals and proximal phalanges of each hand x-‐ray using the OsiriX®

software. The measurements are analysed using univariate statistics and cross-‐

validated direct and stepwise discriminant function analysis. In the adult sample,

all of the hand bone measurements were significantly dimorphic with a tendency

for the width measurements to express a higher degree of sexual dimorphism than

the length measurements. A maximum classification accuracy of 91.00% was

achieved (using a stepwise discriminant function consisting of 8 measurements)

with a sex bias of -‐6.00%. Analysis of the sub-‐adult data suggested that the hand

bones start to become metrically sexually dimorphic between the ages of 14 to 15

years; stepwise discriminant function analysis produced a maximum classification

accuracy of 95.00%, with a sex bias of 10.00%. However, when attempting to

classify sex in sub-‐adults using an adult function, males were likely to be

misclassified as females, and the highest classification achieved was 65.00% with a

sex bias of -‐35.00%. This would suggest that the functions developed using adult

ABSTRACT

iv

data are not forensically viable for sex estimation in sub-‐adult skeletal remains in a

Western Australian population.

The results of the current study indicate that sex can be accurately estimated

based on the morphometric analysis of hand bones in a Western Australian

population. The cross-‐validated classification accuracies are both within the

acceptable range of classification accuracies previously published for sex

estimation based on morphometric hand bone data and comparable to

classification accuracy ranges found for other skeletal elements such as the skull. It

also demonstrates that the hand bones start to express sexual dimorphism in sub-‐

adults from the age of 14 years. The standards produced from this study can be

used in forensic investigations that require sex estimation standards specific to a


ACKNOWLEDGEMENTS

v

ACKNOWLEDGEMENTS

This project, which has been overwhelming at times, has been made possible by

the never-‐ending support of my family, peers and supervisors. I will now take a

moment to express my gratitude and utmost appreciation to anyone that has

helped me along the way over this past year.

Professional Acknowledgements

First, I would like to thank my co-‐ordinating supervisor, Professor Daniel Franklin

and my co-‐supervisor, Miss Ambika Flavel for their patience, understanding and

the wealth of knowledge they did not hesitate to share throughout the duration of

my post-‐graduate studies. As a team, they were able to provide constructive

criticism that was both balanced and productive. I would have not been able to

reach my full potential without their efforts

I would also like to thank those that have provided academic and administrative

support during my time at the Centre for Forensic Science; in particular Professor

Ian Dadour, the director of the Centre for Forensic Science, Mr Algis Kuliukas and

Miss Bonnie Knott. I am grateful for the resources made accessible by the Centre of

Forensic Science to complete this research and also for the technical support

readily available when any issues arose. Thank you to Adjunct Associate Professor

Robin Hart as well, for providing digital hand x-‐rays from the Picture Archiving

and Communication Systems (PACS) database with efficiency and resolving any

issues that occurred without hesitation.

Lastly, I would like to thank my peers; specifically Alex, Elsie, Siobhan and other

members of the ‘forensic anthropology research group’ at the Centre for Forensic

Science. My peers not only provided different perspectives and academic

assistance, they made this year of post-‐graduate study seem less impossible.

ACKNOWLEDGEMENTS

vi

Personal Acknowledgements

First and foremost I would like to thank my family; my mum and sister, Michelle

and Taylor, for being incredibly supportive for the entire duration of my tertiary

studies and for their undying love, encouragement and patience. Special thanks

also goes to Dave for his contagious optimism and for always showing a genuine

interest in this research project. I also wish to acknowledge my partner David for

listening to my intolerable ramblings and always providing a shoulder to lean on.

To all of the above people, thank you!

Finally, to family, friends and anyone that was the victim of one of my many thesis

tangents, thank you for listening.

TABLE OF CONTENTS

vii

TABLE OF CONTENTS

DECLARATION ......................................................................................................................... i

ABSTRACT .............................................................................................................................. iii

ACKNOWLEDGEMENTS ........................................................................................................ v

Professional Acknowledgements ............................................................................................. v

Personal Acknowledgements .................................................................................................... vi

TABLE OF CONTENTS ........................................................................................................ vii

LIST OF FIGURES ................................................................................................................... xi

LIST OF TABLES ................................................................................................................. xiii

CHAPTER ONE ......................................................................................................................... 1

Introduction: research objectives and outline ............................................................ 1 1.1 The modern role of forensic anthropology .................................................................... 1

1.2 The estimation of skeletal sex ............................................................................................ 2

1.2.1 Sex estimation potential of the metacarpals ......................................................................... 3

1.3 Research aims .......................................................................................................................... 4

1.4 Data collection ......................................................................................................................... 5

1.5 Limitations ................................................................................................................................ 6

1.6 Thesis outline ........................................................................................................................... 7

CHAPTER TWO ....................................................................................................................... 9

A brief introduction to the anatomy of the hand ........................................................ 9

2.1 Introduction .............................................................................................................................. 9

2.2 Skeletal anatomy of the hand .............................................................................................. 9

2.3 Anatomical position and directionality ......................................................................... 10

2.4 Muscles acting on the hand ................................................................................................ 12

2.4.1 Anterior compartment of the forearm ................................................................................. 12

2.4.2 Posterior compartment of the forearm ................................................................................ 16

2.4.3 Intrinsic muscles of the hand ................................................................................................... 19

CHAPTER THREE ................................................................................................................ 21

Sexual dimorphism and sex estimation methods: a review of previous

research ................................................................................................................................. 21 3.1 Introduction ............................................................................................................................ 21

TABLE OF CONTENTS

viii

3.2 Sexual dimorphism .............................................................................................................. 21

3.2.1 The pelvis .......................................................................................................................................... 22

3.2.2 The skull ............................................................................................................................................. 23

3.2.3 Long bones ........................................................................................................................................ 24

3.3 Sex estimation based on the analysis of the hand ..................................................... 24

3.3.1 Fleshed hand .................................................................................................................................... 25

3.3.2 Hand bones ....................................................................................................................................... 28

CHAPTER FOUR ................................................................................................................... 33

Materials and Methods ..................................................................................................... 33

4.1 Introduction ........................................................................................................................... 33

4.2 Materials ................................................................................................................................. 33

4.3 Methods ................................................................................................................................... 34

4.3.1 Landmark definitions and typology ....................................................................................... 34

4.3.2 Measurement definitions ........................................................................................................... 36

4.3.3 Measurement acquisition – OsiriX® ....................................................................................... 37

4.4 Statistical analyses: precision test ................................................................................. 39

4.5 Statistical analyses: measurement data ....................................................................... 42

4.5.1 Normality tests ................................................................................................................................ 42

4.5.2 Significance tests ............................................................................................................................ 43

4.5.3 Discriminant function analyses ............................................................................................... 44

CHAPTER FIVE ..................................................................................................................... 49

Results .................................................................................................................................... 49

5.1 Introduction ........................................................................................................................... 49

5.2 Measurement precision ..................................................................................................... 49

5.3 Descriptive statistics for the adult data ........................................................................ 52

5.3.1 Age distribution .............................................................................................................................. 52

5.3.2 Measurement Normality ............................................................................................................. 52

5.3.3 Univariate comparisons .............................................................................................................. 53

5.3.4 Discriminant function analyses ............................................................................................... 56

5.3.5 Posterior probabilities ................................................................................................................. 58

5.4 Population differences ....................................................................................................... 62

5.4.1 Measurement differences ........................................................................................................... 62

5.4.2 Variation in the expression of sexual dimorphism ......................................................... 63

5.4.3 Classification accuracy ................................................................................................................. 64

5.5 Sub-‐adult analyses ............................................................................................................... 65

TABLE OF CONTENTS

ix

5.5.1 Age distribution .............................................................................................................................. 65

5.5.2 Measurement normality ............................................................................................................. 65

5.5.3 Univariate comparisons .............................................................................................................. 66

5.6 Sex classification accuracy in the sub-‐adult hand ...................................................... 66

5.7 Interaction effects ................................................................................................................. 68

CHAPTER SIX ........................................................................................................................ 69

Discussion and conclusions ............................................................................................ 69

6.1 Introduction ............................................................................................................................ 69

6.2 Measurement precision ...................................................................................................... 69

6.3 Adult data ............................................................................................................................... 71

6.3.1 Sexual dimorphism in the hand ............................................................................................... 71

6.3.2 Morphometric population variation ...................................................................................... 76

6.3.3 Importance of population specific standards .................................................................... 83

6.4 Sub-‐adult sample .................................................................................................................. 84

6.5 Forensic applications .......................................................................................................... 86

6.6 Limitations and future research ...................................................................................... 87

6.7 Conclusions ............................................................................................................................. 88

REFERENCES ........................................................................................................................ 91

Appendix One .................................................................................................................... 105

Appendix Two ................................................................................................................... 109

Appendix Three ................................................................................................................ 111

Appendix Four ................................................................................................................... 113

LIST OF FIGURES

xi

LIST OF FIGURES

Figure 2.1 10

Dorsal view of the bones of the right hand.

Figure 2.2 11

Dorsal view of the left hand with terms used to define the anatomical

position of the hand.

Figure 2.3 13

The most superficial layer of muscles in the anterior forearm.

Figure 2.4 15

The second layer of muscle in the anterior forearm.

Figure 2.5 15

The third layer of muscle in the anterior forearm.

Figure 2.6 17

The lateral superficial layer of muscles in the posterior forearm.

Figure 2.7 18

The medial superficial layer of muscles in the posterior forearm.

Figure 2.8 19

The deep muscle layer of posterior forearm.

Figure 4.1 36

Antero-‐posterior view of metacarpal two, metacarpal one and

proximal phalanx one (from left to right) illustrating the eight

landmarks defined in Table 4.2..

Figure 4.2 38

Antero-‐posterior view of metacarpal two illustrating the

measurements acquired for the study; defined in Table 4.3.

LIST OF FIGURES

xii

LIST OF TABLES

xiii

LIST OF TABLES

Table 2.1 11

Terms used to describe anatomical positioning.

Table 4.1 34

The number of individuals in each age group in the sub-‐adult sample.

Table 4.2 37

Definitions of the landmarks acquired for metacarpal two.

Table 4.3 38

Definitions of acquired measurements for metacarpal two.

Table 5.1 50

Measurement precision (TEM, rTEM & R) for the landmark

measurement method.

Table 5.2 51

Measurement precision (TEM, rTEM & R) for the line-‐tool

measurement method.

Table 5.3 53

Distribution of age (in years) of the adult Western Australian sample.

Table 5.4 54

Descriptive statistics and means comparison of mean hand bone

measurements (in mm).

Table 5.5 57

Direct single variable discriminant analyses of individual hand

bones, including demarking point values (in mm).

Table 5.6 57

Direct multiple variable discriminant analysis of metacarpals.

LIST OF TABLES

xiv

Table 5.7 59

Stepwise discriminant function analysis of the Western Australian

adult sample.

Table 5.8 63

Five comparative populations (including source of data and sample

size).

Table 5.9 64

Classification accuracies when applying foreign standards to a


Table 5.10 65

Distribution of age (in years) for each of the three age groups within

the sub-‐adult data sample.

Table 5.11 67

Stepwise discriminant functions based on the analysis of the sub-‐

adult sample.

Table 5.12 68

Sex classification accuracies of adult Function 13 to the sub-‐adult

sample.

Table 6.1 75

Sex classification accuracy of hand bone measurements in a variety

of global populations.

Table 6.2 78

Quality of life statistics, quality of life index and human development

index for each of the five comparative populations.

Table 6.3 82

Year of birth and year of death ranges of the present study and the

three temporally different comparative studies.

LIST OF TABLES

xv

Table A1.1 105

Posterior probabilities calculated for the adult discriminant

Functions 1 to 18.

Table A2.1 109

Unpaired t-‐test results for the comparison of metacarpal one, two

and four lengths from the current study to four previously published

studies.

Table A3.1 111

Unpaired t-‐test results for the comparison of males and females for

each of the five comparative populations.

Table A4.1 113

Descriptive statistics of mean sub-‐adult hand bone measurements (in

mm) for Group A.

Table A4.2 117


mm) for Group B.

Table A4.3 121


mm) for Group C.

1

CHAPTER ONE

Introduction: research objectives and outline

1.1 The modern role of forensic anthropology

The use of forensic anthropology in medico-‐legal investigations has become more

common over time, with an increasing number of referred cases involving remains

that are ‘problematic’ for a forensic pathologist (Braz 2009; Dirkmaat et al. 2008).

These ‘problematic’ cases include those, for example, requiring the assessment of

skeletal, partially fleshed, charred or dismembered remains. Forensic

anthropology is the application of concepts derived from the theory and methods

of physical anthropology to a forensic investigative context that requires the

analysis of skeletal remains (Cattaneo 2007; Dirkmaat et al. 2008). The analysis of

human skeletal remains by a forensic anthropologist involves (amongst other

factors) the construction of a biological profile, which aids in establishing a

possible identity in conjunction with missing persons information and other lines

of forensic evidence (SWGANTH 2011; Scheuer 2002).

A biological profile involves estimating sex, age, stature and ancestry through

metric and non-‐metric analyses of skeletal remains. Prior to constructing a

biological profile the forensic anthropologist must first confirm that the remains

are in fact bone and then establish whether or not the skeletal remains are of

human origin (Bass 2005). Once the remains are confirmed to be human in origin,

a biological profile is formulated, which will essentially narrow the pool of possible

matching identities. For example, by estimating sex, a forensic anthropologist

eliminates all individuals present in the potential pool that are of the opposite sex.

As the primary focus of the present thesis is on methods for skeletal sex

estimation, the latter is considered in more detail below.

CHAPTER ONE

2

1.2 The estimation of skeletal sex

The estimation of skeletal sex is generally the first component of a biological

profile performed because statistics for estimating other biological attributes (e.g.

age, stature and ancestry) are generally sex-‐specific (Braz 2009; Franklin 2010).

Sexual dimorphism is the biological foundation of sex estimation and is defined as

physical and behavioural differences occurring between males and females

(Glücksman 1981; Frayer & Wolpoff 1985). Sex differences in the shape, size and

appearance of bones arise during development according to individual genetic

markers and in response to sex hormones during puberty. This is due to bone

development being dependent on a combination of genetic markers and hormone

exposure (Frayer & Wolpoff 1985). The age at which these sex-‐specific

morphological changes occur is dependent on a number of genetic and

environmental factors that are population specific (Frayer & Wolpoff 1985).

Skeletal sex is estimated using both a metric and/or non-‐metric assessment.

Metric analyses involve taking a series of skeletal measurements that are

compared to pre-‐existing standards relevant to the population concerned (Braz

2009; Stojanowski 1999). The visual analyses of morphological traits (such as the

shape, size and specific bony protrusions or features) are used to estimate sex by

comparing these morphological traits to those that are known to be male or female

(White & Folkens 2005). For example, with regard to sex estimation, non-‐metric

analyses involve the visual assessment of the gross morphology of the pelvis or

skull, which are both known to be sexually dimorphic. More specifically, with

regards to the pelvis, such an assessment of traits could involve applying the

Phenice (1969) method. This method is based on assessing whether certain

morphological traits (ventral arc; subpubic concavity; medial aspect of the

ischiopubic ramus) are present in the pelvis (Phenice 1969). Metric analyses of

those same bones would involve taking defined measurements and inputting them

into a discriminant standard; the values obtained are used to statistically assign an

unknown to a particular group (e.g. male or female) (Roussas 1997).

As the expression, magnitude and age at initial appearance of sexual dimorphism

varies between populations, sex estimation standards are required to be

CHAPTER ONE

3

population specific (Franklin et al. 2012a; Lazenby 2002). Standards used to

classify morphometric data according to sex are most accurate when applied to the

population from which they are derived (Lazenby 2002; Burrows et al. 2003). This

is based on evidence that males and females can be more or less dimorphic within

a given population (Franklin et al. 2012b;Walker 2008; SWGANTH 2011).

1.2.1 Sex estimation potential of the metacarpals

Although the pelvis and the skull are considered to be the most sexually dimorphic

bones and therefore preferred for sex estimation, recent research has worked

towards quantifying the sex estimation potential of other bones. For example, it

has been demonstrated that the sternum (Franklin et al. 2012b), femur (Asala et al.

2004), metatarsals (Robling & Ubelaker 1997) and metacarpals (Case & Ross

2007) can be used to correctly classify sex with a high degree of expected accuracy

(above 80%) and they thus have obvious forensic potential. Differential

preservation of remains (or the absence of the pelvis and cranium) can make sex

estimation more difficult. In response to this, this thesis will focus on the sex

estimation potential of the metacarpals of the hands as data can be readily

acquired from radiographs and their exhibited ‘resistance’ to decomposition. Due

to their small tubular structure, metatarsals and metacarpals often exhibit less

postmortem damage at recovery (compared to larger appendicular bones) and are

known to be sexually dimorphic (in particular their interarticular length and

breadth).

Previous research specifically examining the sex estimation potential of the

metacarpals have all reported accuracy rates above 80%; for example; Scheuer and

Elkington 1993 (British population); Falsetti 1995; Stojanowski 1999; Case and

Ross 2007 (American populations); Barrio 2006 (Spanish population); and

Khanpetch et al. 2011 (Thai population). However, Burrows et al. (2003) showed

that the application of skeletal standards based one population to another can lead

to the statistical misclassification of sex. The discriminant function standards from

the previous research of Scheuer and Elkington (1993), Falsetti (1995) and

Stojanowski (1999) were applied to a sample group consisting of modern

American cadavers that had died no more than three years prior to the study

CHAPTER ONE

4

(Burrows et al. 2003). The accuracy of those standards ranged from 63 to 95%.

The differences in the range of accuracies between the ‘foreign standards’ and the

study conducted by Burrows et al. (2003) can be attributed to population and

temporal differences between the samples used in the different studies. The errors

highlighted by Burrows et al. (2003) have a potentially significant impact on the

admissibility of anthropological evidence.

1.3 Research aims

i) To statistically quantify the accuracy and reliability of two measurement methods of acquiring measurements in digital x-‐rays

Prior to data collection, an intra-‐observer precision test must be conducted to

quantify the accuracy and reliability of using either a landmark or line-‐measure

approach to measure hand bones in digital x-‐rays. The former method requires the

identification of defined landmarks that are then mathematically transformed to

acquire linear inter-‐landmark measurements. In contrast, the latter method only

requires a line to be drawn between two landmarks that provides a direct

measurement value (See Chapter Four for more details). Based on the results of

the intra-‐observer precision test, the method that is the most reliable, accurate and

practical will be used for all subsequent data collection.

ii) To quantify the expression and magnitude of sexual dimorphism in the hand bones (metacarpals and phalanges)

Although the pelvis and cranium are considered to be the most sexually dimorphic

bones, the appendicular skeleton can also be used to estimate sex. Scheuer and

Elkington (1993), Falsetti (1995) and Case and Ross (2007) have all conducted

studies resulting in the production of discriminant functions for estimating sex in

the metacarpals and phalanges that correctly classify sex with up to 87% accuracy.

However Lazenby (2002) and Burrows et al. (2003) demonstrated that

discriminant functions based on one population cannot be applied to ‘foreign’

populations because a loss of accuracy ensues. To this end, the present study will

assess sexual dimorphism in the metacarpals and phalanges of Western Australian

CHAPTER ONE

5

individuals. The main aim is to formulate population specific sex estimation

standards for this population, as they do not currently exist.

iii) To statistically quantify the age at which the hand bones are metrically sexually dimorphic

Some sexually dimorphic traits in the size, shape and behaviour of males and

females are evident in the early stages of development. However, the majority of

the differences that occur between the sexes are known to develop under

hormonal influences during puberty (Frayer & Wolpoff 1985). If the latter applies

to the development of the hand bones, one would expect sex specific

morphological differences in the finger bones to manifest at around 14 or 15 years

of age (Schwartz 2007). This study will investigate the minimum age at which sex

can be reliably estimated in the hand bones and, therefore, the age at which

discriminant functions based on a Western Australian population can be

accurately applied in a forensic context.

1.4 Data collection

The sample for the current study consists of 300 antero-‐posterior digital hand x-‐

rays (150 males and 150 females) of adult individuals. These hand x-‐rays are

acquired from the Picture Archiving and Communication Systems (PACS) database,

which contains medical scans from various Western Australian hospitals. A further

sub-‐set of younger individuals are also examined; 100 hand-‐wrist digital x-‐rays of

individuals between the 13 to 18 years of age. Approximately 20 x-‐rays are

acquired for each age group between the ages of 13 and 18 years inclusive; this

sample was used to explore the age at which the metacarpals and phalanges are

quantifiably sexually dimorphic (see above). Only radiographs of hands that show

little (or no) skeletal trauma or anomalies in the metacarpals and proximal

phalanges are used. Additional to these requirements, the selected radiographs

had to clearly show the landmarks that define the required measurements. The

hand x-‐rays obtained from the PACS database are anonymised with only age and

sex data retained. The specific ancestry of each individual is not known, but the

sample is taken as approximating the current Western Australian population. This

CHAPTER ONE

6

study was approved by the Human Research Ethics Committee of the University of

Western Australia; Project Number RA/4/1/4362.

1.5 Limitations

As with any research, there are limitations with this study that must be taken into

consideration. Firstly, the ancestry of an individual is not recorded when medical

x-‐rays are conducted, as it not a medical requirement. The Western Australian

sample examined in the present study, are thus assumed to be representative of

the current Western Australian population. The Western Australian population

comprises 3.1% indigenous Australians, which is higher than the Australian

average (2.5%). The Australian Bureau of Statistics (Australian Bureau of Statistics

2013) data indicate that 56.2% of the WA population have one or more parent

born overseas and 75% have an ancestry other than Australian (within 2

generations). This compares with Australia as a whole where 46.2% of people have

one or more parent born overseas. In broad terms of ancestry (Australian Bureau

of statistics 2011), the population is predominantly Caucasian in all Australian

states (but not territories).

The second limitation is the effect of skeletal maturation and degeneration; data

collection involves acquiring the maximum length or width measurements of the

hand bones. It is thus required that the digital x-‐rays examined are from

individuals who have reached skeletal maturity which ensures that the hand bones

are of their maximum dimensions and epiphyseal fusion has occurred. As an

individual ages, degenerative bone changes can occur which would also affect the

acquisition of the maximum dimensions of the hand bones (through age-‐related

bone degeneration or loss such as osteopenia or osteoporosis). For the this reason,

the age range of the adult sample was limited to between 18 and 67 years of age,

thus generally ensuring skeletal maturity and the avoidance of degenerative bone

changes. Hand x-‐rays belonging to individuals over 65 years of age were subject to

rigorous scrutiny, to make sure the linear measurements required were

adequately represented.

CHAPTER ONE

7

1.6 Thesis outline

This study is presented in six chapters, the first being an introduction to the

background of this study and the purpose of this thesis; to develop sex estimation

standards for a Western Australian population based on measurements taken from

the metacarpals. Chapters Two covers the basic anatomy of the hand bones

including anatomical terminology, directionality and muscles associated with the

hand. Chapter Three considers sex estimation methods, and more specifically

previous studies involving sex estimation of hand bones. Chapter Four is the

materials and methods section that outlines the data collection and analysis

protocols. The results of this study and the subsequent discussion and conclusions

are presented in Chapters Five and Six respective

CHAPTER ONE

8

9

CHAPTER TWO

A brief introduction to the anatomy of the hand

2.1 Introduction

The present study concerns with the sex estimation potential of the hand bones

within a Western Australian population; the latter accordingly requires an

understanding of basic hand anatomy. This chapter will outline the skeletal and

muscular components of the hand, in addition to providing an understanding of

terminology used to describe anatomical position, directionality and muscle

movements in that particular region.

2.2 Skeletal anatomy of the hand

The hand is anatomically defined as the terminus of the upper limb, with each

hand consisting of 27 bones (White et al. 2012). These 27 bones are sub-‐divided

into three groups: the wrist or the carpus, the palm or metacarpus, and the fingers

or phalanges (Figure 2.1) (Schwartz 2007; Gray 2010). There are eight carpal (or

wrist) bones that provide the foundation for the five digits of the hand. Two carpal

bones in particular (the scaphoid and lunate) articulate with the radius and form

the wrist joint (Gray 2010).

The next segment of the hand (the metacarpus) comprises five metacarpals within

the palm of the hand; metacarpal 1 (thumb side) through metacarpal 5. The

metacarpals are long tubular bones with a rounded distal articular surface (head)

and a more rigid proximal articular surface (base)(White et al. 2012; Gray 2010).

Each metacarpal can be distinguished by their size and specific morphological

characteristics present in the base of each bone. (White et al. 2012).

CHAPTER TWO

10

Figure 2.1 Dorsal view of the bones of the right hand: (a) phalanges;

(b) metacarpus; (c) carpus. Source: White et al. 2012

The third and last segment of the hand is made up of the phalanges (fingers). Each

finger has three phalanges; the proximal, medial and distal phalanx (Schwartz

2007; White et al. 2012). The thumb, however, only has a proximal and distal

phalanx (Figure 2.1) (White et al. 2012).

2.3 Anatomical position and directionality

The anatomical position is an orientation of the human body where it is displayed

as standing upright with both feet facing forward and arms are extended along the

sides of the torso with palms facing forward (Ramones 1986). It is this position

that acts as a reference for describing parts of the body in relation to each other, or

in what direction they are facing. For instance if an organ or bone is in front of

another, it is described as anterior (or ventral) that reference point. Table 2.1

below outlines these directional terms.

In describing the hand bones in this study, the thumb (or first digit) is lateral and

the little finger (or fifth digit) is medial, see Figure 2.2. The heads of the

metacarpals and phalanges are the distal ends of the bones; the bases are thus the

proximal ends.

CHAPTER TWO

11

Table 2.1 Terms used to describe anatomical positioning (Source: Ramones

1986)

Anatomical term Description

Anterior or Ventral In front of

Posterior or Dorsal Behind

Superior or Cranial (towards the head) Above

Inferior or Caudal (towards the tail) Below

Medial Towards the midline

Lateral Further from the midline

Proximal End closest to the head or torso

Distal End furthest from the head or torso

Figure 2. 2 Dorsal view of the left hand with terms used to define the

anatomical position of the hand bones. Source: Bass 2005

Distal

Proximal

Lateral Medial

CHAPTER TWO

12

2.4 Muscles acting on the hand

The muscles that act on the hand originate in the forearm and are generally

considered to be a part of either the anterior or posterior compartments of the

forearm (Ramones 1986; Agur & Dalley 2009). Muscle positions are described

according to their origin and insertion, which are the points of attachment of

muscles to bone or fascia. The origin of a muscle is the point from which a muscle

arises and is considered to be the fixed point of attachment with regards to

movement (Gray 2010). Insertion refers to the point at which a muscle ends (or

inserts) and is the point of skeletal movement (Gray 2010).

Muscles allow for several movements along the joints of the upper and lower

limbs, which include flexion, extension, abduction, adduction, pronation and

supination (Gray 2010; Ramones 1986). Flexion is the movement of articulating

bones that acts to decrease the angle between the bones that make up a joint.

Conversely, extension is the movement that increases the angle between the

articulating bones (Gray 2010; Leversedge et al. 2010). Abduction is the lateral

movement of bones away from the mid-‐line and adduction is when a movement

results in moving a body part toward the mid-‐line. Pronation and supination are

rotation movements with pronation used to describe rotation towards the mid-‐line

and supination used to describe rotations away from the midline (Gray 2010; Agur

& Dalley 2009).

The muscles in the anterior compartment of the forearm and the palmar side of the

hand are generally flexors and pronators, whilst the muscles found in the posterior

compartment of the forearm and the dorsal side of the hand are extensors and

supinators (Gray 2010; Cael 2010). The main muscles that act on the hand at both

the carpometacarpal (wrist) and metacarpophalangeal (knuckle) joints are

outlined below.

2.4.1 Anterior compartment of the forearm

The anterior compartment of the forearm consists of muscles that act to flex (or

pronate) the hand at the wrist and the knuckle joint (Cael 2010; Leversedge et al.

2010). There are two layers of muscles in the anterior compartment of the

CHAPTER TWO

13

forearm; superficial and deep. The superficial layer is the layer of muscles closest

to the skin and consists of flexor carpi ulnaris, flexor carpi radialis, flexor digitorum

superficialis, and palmaris longus (Figure 2.3). The muscles in this superficial layer

have a common flexor origin, which is the medial epicondyle of the humerus. The

deep layer of muscles lies beneath the superficial layer and consists of flexor

digitorum profundus and flexor pollicis longus.

Figure 2.3 The most superficial layer of muscles in the anterior

forearm. Source: Agur & Dalley (2009).

Flexor carpi ulnaris is the most medial of the superficial layer of muscles within the

anterior compartment of the forearm (Ramones 1986; Agur & Dalley 2009). This

muscle has two heads that arise from two origins; the humeral head starts from

the medial epicondyle of the humerus and the ulnar head starts from the olecranon

process of the ulna (Cael 2010; Gray 2010). The muscle runs from these two points

of attachment to two of the carpal bones (pisiform and the hook of hamate), as well

as the base of metacarpal five. Flexor carpi ulnaris acts on the hand by allowing

flexion and adduction at the wrist (Cael 2010; Agur & Dalley 2009).

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14

Flexor carpi radialis (like flexor carpi ulnaris) also allows flexion at the wrist joint

and is positioned on the lateral side of the anterior compartment of the forearm

(Cael 2010; Gray 2010; Agur & Dalley 2009). Flexor carpi radialis, however, only

has the one head arising from the medial epicondyle of the humerus and inserts at

the base of metacarpals two and three (Leversedge et al. 2010). As it is located on

the lateral side of the forearm, flexor carpi radialis abducts the hand at the wrist

joint.

Palmaris longus is a muscle found between flexor carpi ulnaris and flexor carpi

radialis that flexes the hand at the wrist joint (Cael 2010). Palmaris longus runs

from the medial epicondyle of the humerus to the flexor retinaculum and palmar

aponeurosis. The flexor retinaculum is a fibrous band (or ligament) that covers the

carpus and the palmar aponeurosis is a layer of fibrous tissue found in the palm of

the hand (Leversedge et al. 2010; Cael 2010; Gray 2010).

Flexor digitorum superficialis (Figure 2.4) also originates from the common flexor

origin and has two additional heads that arise from the coronoid process of the

ulna and the radial tuberosity of the radius (Cael 2010; Leversedge et al. 2010).

Flexor digitorum superficialis inserts into medial phalanges two through five and

allows flexion at the interphalangeal joints and metacarpophalangeal joints of

digits two through five.

Flexor digitorum profundus (Figure 2.5) runs from the medial surface of the

proximal region of the ulna and the interosseous membrane (the fibrous joint

between the ulna and the radius) and inserts at the base of distal phalanges two

through five (Leversedge et al. 2010; Gray 2010). As it inserts at the base of the

distal phalanges, flexor digitorum profundus mainly acts to flex the fingers at the

distal interphalangeal joint. However, the latter muscle also aids in the flexion of

the proximal phalangeal and the metacarpophalangeal joints of the fingers.

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15

Figure 2.4 The second layer of muscle in the anterior forearm. Source:

Agur & Dalley (2009).

Figure 2.5 The third layer of muscle in the anterior forearm. Source:

Agur & Dalley (2009).

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16

Flexor pollicis longus (Figure 2.5) allows flexion of the thumb at the

carpometacarpal, metacarpophalangeal and intercarpal joints (Cael 2010; Gray

2010; Agur & Dalley 2009). Flexor pollicis longus inserts at the anterior surface of

the radius and the interosseous membrane and attaches at the base of the distal

phalanx located in the thumb.

2.4.2 Posterior compartment of the forearm

The muscles in the posterior compartment of the forearm work opposite to those

in the anterior compartment -‐ rather than flex and pronate the posterior forearm

muscles extend and supinate (Gray 2010; Cael 2010; Agur & Dalley 2009). The

muscles are separated into three main groups: the lateral superficial; the medial

superficial; and the deeper muscle groups.

The lateral superficial group consists of extensor carpi radialis longus and extensor

carpi radialis brevis (Figure 2.6). Extensor carpi radialis longus is the longer muscle

and extends from the lateral supracondylar ridge of the humerus to the base of

metacarpal two (Cael 2010). Extensor carpi radialis brevis arises just inferior to the

origin of extensor carpi radialis longus (at the lateral epicondyle of the humerus)

and inserts at the base of metacarpal three (Leversedge et al. 2010; Cael 2010).

Both muscles extend and abduct the hand at the wrist joint.

The medial superficial group of muscles in the posterior compartment of the

forearm includes extensor carpi ulnaris (Figure 2.6), extensor digitorum and

extensor digiti minimi (Figure 2.7). These muscles allow extension of the hand at

the wrist joint, with additional movement specific to their insertions (Ramones

1986). Extensor carpi ulnaris originates in the lateral epicondyle of the humerus

and inserts at the base of metacarpal five. Additional to extension, it also acts to

adduct the hand at the wrist joint (Cael 2010; Agur & Dalley 2009). The lateral

epicondyle is also the origin for extensor digitorum and extensor digiti minimi.

Extensor digitorum inserts at the bases if the middle phalanges and distal

phalanges two through five. It extends the fingers at the metacarpophalangeal,

proximal interphalangeal and distal interphalangeal joints (Cael 2010; Gray 2010).

Extensor digiti minimi inserts at the base of proximal phalanx five and extends the

little finger at both the metacarpophalangeal and interphalangeal joints.

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17

Figure 2.6 The lateral superficial layer of muscles in the posterior

forearm. From left to right: Extensor carpi radialis longus; Extensor carpi

radialis brevis; and Extensor carpi ulnaris. Source: Agur & Dalley (2009).

The final group of muscles in the posterior compartment of the forearm are the

deeper muscles that include extensor indicis, extensor pollicis longus, extensor

pollicis brevis and abductor pollicis longus (Figure 2.8). Extensor indicis runs from

the posterior surface of the ulna and the interosseous membrane and inserts at the

base of proximal phalanx two and extensor indicis extends the index finger (Cael

2010).

Extensor pollicis brevis and longus extend the thumb by inserting at the base of

proximal and distal phalanx one respectively. Both stem from the interosseous

membrane, however extensor pollicis brevis also attaches to the posterior surface

of the radius, and extensor pollicis longus attaches to the posterior surface of the

ulna (Cael 2010; Agur & Dalley 2009). Extensor pollicis brevis has the additional

role of extending (abducting) the thumb.

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18

Figure 2.7 The medial superficial layer of muscles in the posterior

forearm. From left to right: Extensor digiti minimi: Extensor digitorum.

Source: Agur & Dalley (2009).

Abductor pollicis longus further assists extensor pollicis brevis in both the extension

and abduction of the carpometacarpal joint in the thumb. Abductor pollicis longus

originates from the posterior surface of the ulna, radius and interosseous

membrane and insets at the base of metacarpal one (Gray 2010; Cael 2010; Agur &

Dalley 2009).

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19

Figure 2.8 The deep muscle layer of the posterior forearm. From left to

right: Extensor indicis; Extensor pollicis longus; Extensor pollicis brevis;

Abductor pollicis longus; Supinator; and Anconeus. Source: Agur &

Dalley (2009).

2.4.3 Intrinsic muscles of the hand

In addition to the forearm muscles, there are muscles that allow more flexion (or

abduction) of the thumb and little finger. These intrinsic muscles facilitate

specialised movement of the fingers, which in turn allow precision gripping,

grasping and opposition of the thumb (Cael 2010). The intrinsic muscles of the

hand are either hypothenar or thenar muscles; hypothenar muscles acting on the

little finger and thenar muscles acting on the thumb (Ramones 1986; Cael 2010).

The hypothenar muscles include opponens digiti minimi, flexor digiti minimi brevis

and abductor digiti minimi which allow opposition, flexion and abduction of the

little finger (Cael 2010; Gray 2010). The thenar muscles consist of opponens

pollicis, flexor pollicis brevis and abductor pollicis brevis which allow opposition,

flexion and abduction of the thumb (Cael 2010; Agur & Dalley 2009).

21

CHAPTER THREE

Sexual dimorphism and sex estimation methods: a review of

previous research

3.1 Introduction

Sex estimation is an integral component in constructing a biological profile from

skeletal tissue and has been extensively researched using both metric and non-‐

metric approaches. This chapter discusses the concept of sexual dimorphism and

the expression of sexual dimorphism in skeletal elements, followed by a review of

literature specifically concerning sex estimation methods based on the analysis of

the fleshed hand and its bones.

3.2 Sexual dimorphism

Sexual dimorphism is the behavioural and physical difference (other than the

reproductive organs and genitalia) that occurs between males and females within

a species (Frayer & Wolpoff 1985; Glücksmann 1981; Park 2013). In general these

differences relate to the size and robusticity of males and females within a species

-‐ in hominid species males generally being larger than females (Krogman 1978;

Park 2013). The extent to which sexual dimorphism is expressed differs between

hominid species, but it can also differ within a species, as the expression and

magnitude of sexual dimorphism is affected by multiple factors including sexual

selection and socio-‐economic role differences (Frayer & Wolpoff 1985).

Human males (like males of most other hominid species) tend to have a larger

overall body size and exhibit greater muscle mass or robusticity (Plavcan 2001).

This size difference is considered to be a secondary sexual characteristic, however,

it is also evident during postnatal growth (Wells 2007). These size differences are

apparent in bones -‐ most obvious in cortical thickness (Wells 2007). Skeletal

dimorphism can be attributed to the difference in the onset and duration of

puberty between males and females. The male pubertal growth spurt tends to

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22

begin 2 years later than the female pubertal growth spurt (Wells 2007). However,

the male pubertal growth spurt lasts longer than their female counterparts, which

results (on average) in males growing more and for longer than females. (Bogin

1999). The latter can result in size sexual dimorphism. Bones exhibiting clear

sexual dimorphism include the pelvis, skull and long bones; these are discussed

below.

3.2.1 The pelvis

The pelvis is potentially the most sexually dimorphic bone in the skeleton and is

therefore the preferred element for sex estimation (Scheuer 2002; Bruzek 2002;

Reichs 1998). Differences in the male and female pelvis are primarily related to

sex-‐specific functional roles; the pelvis has to accommodate both bipedal

locomotion and childbirth in females. This requires a specific morphology, which

in females includes flared iliac blades, a concave sub-‐pubic angle shape, an obtuse

sub-‐pubic angle and a pelvic inlet that is broad mediolaterally (White et al. 2012;

Plavcan 2001; Reichs 1998). Conversely, male pelvis exhibits morphological traits

that are at the opposite end of the spectrum of the features described above.

Differences in male and female pelvic bones are visually quantifiable; a number of

non-‐metric sex estimation methods are available. Phenice (1969) examined the

pelvic bones of 275 adults from the Terry Collection, which consists primarily of

Caucasian American individuals. A classification accuracy of 96% was reported

based on the assessment of three morphological features in the pubis: the ventral

arc: sub-‐pubic cavity: and ischio-‐pubic ramus ridge. Recently, however, the latter

method has yielded poorer classification results, with accuracies ranging from 60

to 80% when applied to a mixed French and Portuguese population (Bruzek 2002).

Such differences in classification accuracies, however, highlights the population

specific nature of these traits and thus the need for local standards.

The other main approach to quantify pelvic sexual dimorphism is the statistical

analysis of linear measurements. Steyn and Işcan (2008) formulated discriminant

sex estimation functions applicable to a modern Greek population; classification

accuracies ranged from 79.7 to 95.9%. Albanese et al. (2003) examined 232

Portuguese and 324 Caucasian American skeletons and produced logistic

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regression equations based on the premise of having only fragments of the pelvis

and femur available for analysis. This resulted in a correct classification accuracy

of 95%, thus reiterating the well-‐known fact that the pelvis is highly sexually

dimorphic.

3.2.2 The skull

Sex estimation methods utilising measurements (or observations) of cranial

features are generally considered accurate due to high levels of size and shape

dimorphism present in the skull. Difference in muscle size and shape between

males and females are attributable to the increased levels of testosterone that

males are exposed to during puberty, which promotes increased muscle mass

(Glücksmann 1981; Braz 2009). Male skulls (on average) exhibit a larger muscle

mass and thus larger attachment sites such as a more prominent glabella (Frontalis

muscle), heavier temporal crests (Temporalis, digastric and occipitalis muscles),

more pronounced nuchal lines (Frontalis, occipitalis, sternocleidomastoid and

trapezius muscles), square-‐shaped mandible (Masseter, mentalis, temporalis and

mylohyoid muscles), a more robust mastoid (Splenius capitis, longissimus capitis

and sternocleidomastoid muscles) and styloid process (Stylohyoid, styloglossus and

stylopharyngeus muscles) and a large zygomatic arch (Masseter, zygomaticus major

and minor muscles) (Gray 2010; White et al 2012).

One of the earliest morphometric sex estimation studies examined Caucasian-‐

American and African American skulls; a total of eight linear measurements

resulted in a classification accuracy of 82 to 89% (Giles & Elliot 1963). Franklin et

al. (2005), when applying a re-‐defined set of measurements based on those of Giles

and Elliot (1963) to a South African population, found that accuracies were slightly

lower at 77-‐80%. Kranioti et al. (2008) examined a Greek population and reported

a classification accuracy of 88.2%. Variation in classification accuracies across

populations is largely due to differences in the expression and magnitude of

sexually dimorphic cranial traits, thus again highlighting the necessity for

population specific standards.

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3.2.3 Long bones

Krogman (1978, pp.143) defines male long bones as “longer and more massive”

than female long bones. Males, as a result of periosteal apposition during puberty,

tend to have long bones with greater articular end and mid-‐shaft diameters, as

well as greater maximum lengths, compared to females. The average size

difference in long bones is evident in a number of forensic anthropological studies.

For example, Krogman (1978) achieved 80% classification accuracy based on

measurements acquired from the femur and humerus. Discriminant function

analyses of humeral epiphyses and femoral epiphyses in a Greek population have

resulted in classification accuracies above 80% (Kranioti et al. 2009; Kranioti et al.

2011). Calculations based on the measurements of maximum head diameter and

base width of the femur resulted in classification accuracies of 85.7 and 84.3%;

these two measurements were considered the most accurate variables of this

study (Kranioti et al. 2011). The latter studies clearly demonstrate high levels of

sexual dimorphism in a variety of long bones.

3.3 Sex estimation based on the analysis of the hand

The following section is an overview of select literature relating to sexual

dimorphism in the hand, as evidenced through the morphometric analysis of the

fleshed hand and its skeletal structure. Published literature based on the analysis

of the fleshed hand is predominantly concerned with the estimation of stature

rather than sex. However, there are a number of anthropometric studies that have

reported sex differences in the dimensions of the palm and hand. Such studies

provide some degree of useful comparative information for the present study.

Thereafter, research based on the analysis of the hand bones is presented

chronologically; progressing from assessing sexual dimorphism in the hand bones

to producing sex estimation standards that are specific to various global

populations.

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25

3.3.1 Fleshed hand

i) Kanchan et al. (2008)

This study examined the relationship between sex and index-‐ring finger ratios in a

South Indian population. A total of 150 male (18 to 51 years of age) and 150 female

(18 to 45 years of age) individuals were examined from the Karnataka coastal

region in South India. The length of the index and ring fingers were measured in

each hand and the index-‐ring finger ratio was calculated.

The results adhere to the general consensus that males are larger than females,

with mean values for both length measurements being significantly larger in the

former sex. There was no evidence of significant bilateral variation. Sex

classification accuracy (left and right hand) in the male sample was 80%; for

females the classification accuracy for the left hand was slightly higher (78%) than

for the right (74%). The results of this study suggest that the index and ring finger

ratio can be used to classify sex; a ratio of 0.97 or less indicative of males and a

ratio more than 0.97 indicative of females.

ii) Kanchan and Rastogi (2009)

The aim of this study was to establish if hand dimensions and indices were

sexually dimorphic in a North and South Indian population. The sample consisted

of 500 students; 120 males and 100 females from a North Indian population, and

110 males and 170 females from a South Indian population. As the effect of

bilateral asymmetry due to hand dominance was not known, only right-‐handed

students were examined. Hand length, hand breadth and palm length were

measured, from which hand and palm indices were calculated; associated

sectioning points were established and their classification accuracy was reported.

In general, the results of this study showed that male hand dimensions were on

average significantly larger than females for both populations. The highest

classification accuracy was 88.7% for males (hand breadth in the left hand) and

91.9% for females (palm length in the left hand). Index values were found to be

less accurate and therefore not applicable for estimating sex in an Indian

population. Only the index value for the left hand in both males and females was

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26

found to be statistically significant. Using the sectioning point based on the hand

indices, the classification accuracies were only 53.9% and 55.9% for males and

females respectively; the latter indices thus have no forensic value.

iii) Kanchan et al. (2010)

This study is a continuation of Kanchan et al. (2008; see above) albeit the methods

are applied to a South Indian adolescent population. The aim was to evaluate if sex

estimation methods using the index-‐ring finger ratio are applicable to sub-‐adults.

The sample consisted of 175 males and 175 females between 13 to 18 years of age.

In the adolescent sample, the index length was not significantly different between

the sexes. However, the difference in ring finger length was highly significant (p≤

0.001) and there were no significant bilateral difference in either sex. Classification

accuracy for males was 83% in the right hand and 82% in the left hand.

Classification accuracy for females was 74% in the right hand and 80% in the left.

The results from this study appear to suggest that the index-‐ring finger ratio

approach to sex estimation could be applied to a sub-‐adult South Indian

population, as the ratio does not appear to fluctuate due to aging or the cessation

of puberty. It is important to note that accuracy rates were not cross-‐validated,

which perhaps suggests that the classification rates are over-‐inflated.

iv) Aboul-‐Hagag et al. (2011)

The aim of this study was to examine sexual dimorphism in the hand of individuals

drawn from an Egyptian population. Data was acquired from a total of 500

students (250 males and 250 females) from Sohag University; the subjects are

stated to be older than 18 years of age. The age range, mean age or maximum age

of the subjects was not provided. Measurements acquired included hand length

and breadth and index finger and ring finger lengths. Hand index and index-‐ring

finger ratios were also calculated.

Hand length, breadth and the hand index were all significantly larger (p≤ 0.05) in

males for both hands. There was no significant bilateral difference in those

measurements. Classification of sex using the hand index resulted in an accuracy of

80% and 81.2% in males for the right and left hands respectively. Classification of

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27

females was 80% for the right and 78% for the left hand. Classification of sex based

on the index-‐ring finger ratios had higher maximum accuracies for both sexes than

the hand index; 90.4% (male right hand) and 85.6% (female right hand). The

results of this study are consistent with previous research; mean male hand

dimensions were significantly greater than the mean dimensions obtained in

females. The results of this study confirm that the hand is sexually dimorphic in an

Egyptian population, however, the discriminant functions were not cross-‐validated

and thus the stated accuracy may be over inflated.

v) Ishak et al. (2012)

Ishak et al. (2012) assessed whether sex could be accurately estimated using

measurements acquired in hands and handprints for a Western Australian

population. The sample comprised 91 males (age range 19-‐68 years) and 110

females (age range 18-‐63 years). Hand breadth, hand length, palm length and the

lengths of the thumb, middle and ring finger were measured in both hands.

Handprints were acquired using a flatbed scanner and the aforementioned

measurements were also taken in the handprints.

All hand and handprint measurements were found to be statistically significantly

different between males and females (p < 0.001). ANOVA F-‐statistic values suggest

that hand breadth, hand length, palm length, handprint breadth and handprint

length express the greatest sexual dimorphism. Univariate functions were

reported to have expected sex classification accuracies of 82.6% to 94.0%; hand

breadth was considered to be the most sexually dimorphic measurement. A cross-‐

validated stepwise discriminant function analysis resulted in a very high

classification accuracy of 96.5%.

It was concluded that within the Western Australian sample, hand length and hand

breadth are more dimorphic than the lengths of the individual fingers; this accords

with previously published literature (ie. Aboul-‐Hagag et al. 2011). The results also

indicate that anthropometric measurements taken in the hand can be used to

accurately estimate sex in a Western Australian population. The study also offers a

“novel” sex estimation method that can be applied to handprints found in a forensic

context (e.g. at a crime scene).

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28

3.3.2 Hand bones

i) Scheuer and Elkington (1993)

These authors suggest that smaller long bones (such as metacarpals, metatarsals

and phalanges) are more likely to be recovered intact than the larger long bones in

appendicular skeleton. Therefore, the aim of this study was to establish if bones of

the hand are sexually dimorphic and to formulate sex estimation standards. The

sample comprised 60 cadavers (of Caucasian British ancestry) from various

medical schools in the United Kingdom. After defleshing, measurements were

taken of all five metacarpals and the first proximal phalanx. There were six

measurements taken and the acquired data were used to calculate an ‘index of

separation’ (male mean value minus the female mean value, divided by the

combined standard deviation). Measurements that resulted in a higher index of

separation were considered to be more dimorphic. Multiple regression analysis

was used to formulate equations, the performance of which were tested on a

relatively small sample (a hold-‐out sample of 20 individuals).

The measurement that had the highest index of separation was the mediolateral

base of metacarpal two (1.41) followed by the mid-‐shaft width of metacarpal one

(1.29). Overall, the mid-‐shaft widths of all the digits (except metacarpal five) had

indices of separation higher when compared to the other measurements

(maximum length, width of the head and width of the base). The top five

classification accuracies of the test sample ranged from 74% (proximal phalanx

one) to 94% (metacarpal one). A multiple regression equation formulated from

the mid-‐shaft width measurements of each hand bone classified sex at 80%

accuracy. The results of this study suggest that in the hand bones measures of

breadth are more likely to exhibit dimorphism than those of length.

ii) Smith (1996)

The aim of this study was to produce sex estimation models for the hand bones. A

total of 120 individuals were sampled; 40 males and 40 females of both Caucasian

American and African American ancestry groups. The age range of the subjects

was 21 to 50 years. The upper age limit of 50 years was implemented in the

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29

attempt to ensure that bones had not undergone degeneration associated with

advanced age. Antero-‐posterior and mediolateral widths of the head, mid-‐shaft and

base of the five metacarpals and 14 phalanges of each hand were measured.

Maximum lengths were also acquired. Significant bilateral differences were found

in measurements taken in the middle phalanges. Stepwise discriminant function

analysis was conducted and eight classification models produced.

The metacarpal models for both hands were the most accurate (87-‐89%), although

there was a large difference in classification accuracies. The metacarpal models

assigned sex correctly at 89% in the left hand, and at 72% in the right. The phalanx

measurements had a sex classification accuracy of >80%. Smith tested the

accuracy of models produced on opposite hands; classification accuracies reached

86% for the metacarpal models, 81% for the distal phalanges, 78% for the

proximal phalanges and 73% for the middle phalanges. This study concluded that

linear measurements acquired from hand bones can be used to accurately classify

both sex and ancestry; with the models derived from metacarpal data achieving

the highest accuracy.

iii) Stojanowski (1999)

Falsetti (1995) followed the methods of Scheuer and Elkington (1993) and applied

it to a North American population. The aim of the study by Stojanowski (1999) was

to build upon the work of Falsetti by applying the same methods to a larger and

‘more contemporary’ sample. A total of approximately 200 subjects from the

Maxwell Museum of Anthropology were examined. The exact sample composition,

however, was not stipulated; Stojanowski estimated maximum sub-‐sample

numbers as 55 Caucasian-‐American males, 22 African-‐American males, 30

Caucasian-‐American females and 15 African-‐American females. The individuals

studied were of European-‐American and African American ancestry born after

1900; the extent to which this sample is considered ‘contemporary’, however, is

debatable. Stojanowski (1999) also proposed models are able to be used in

fragmented or poorly preserved remains.

No significant population differences were found; sex, however, was significantly

different for all dimensions. Seven discriminant functions for each metacarpal

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30

were produced. The highest classification was achieved using measurements of the

articulate ends of the metacarpals (89%) and the lowest classification was found

using combined measurements (maximum length, base length, head length) for

metacarpals two and five (74%). Although the functions produced are unlikely to

be applicable to a ‘modern’ American population, the functions developed by

Stojanowski (1999) provide sex estimation standards that are suitable for

application in fragmented bones and this has potential forensic utility.

iv) Burrows et al. (2003)

The aim of this study was to test previously published standards (e.g. Scheuer and

Elkington (1993), Falsetti (1995) and Stojanowski (1999)) to determine which is

most accurate. The objective was to establish which metacarpal should be used in

preference for sex estimation. A very small sample of 23 individuals of American

ancestry (it is not stipulated whether they were Caucasian or African American)

were classified using measurements taken from the published literature. It is

important to note that the small sample size may have an affect on the statistical

reliability of the results.

Sex classification accuracy achieved using Scheuer and Elkington (1993) and

Falsetti (1995) was 9% and 5% lower than the rates reported by these authors.

The sex classification accuracies achieved using methods from Stojanowski (1999)

were also slightly different; ranging from 65 to 95% rather than 75 to 90%. This

study essentially highlights the need for sex estimation standards to be both

population specific and contemporary in order to avoid inaccuracies in sex

estimation. The sample used by Scheuer and Elkington (1993) was from a different

population and the sample used by Falsetti (1995) came from a different time

period. These are the likely reasons for the differences in the reported sex

classification accuracies.

v) Barrio et al. (2006)

The aim of this study was to produce sex estimation functions applicable to a

Spanish population. Eight bilateral measurements were taken in 697 metacarpals

of 79 adults (37 male and 42 female). The metacarpals were from a skeletal

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31

collection of individuals who had died between 1975 and 1985; the stated age at

death ranged from 20 to 91 years. Univariate discriminant functions were

produced and were cross-‐validated using a leave-‐one-‐out protocol. A total of 120

discriminant functions were produced based on only one measurement each; 40

functions from the right hand, 40 functions from the left, and 40 based on pooled

side data. The function that had the highest classification accuracy was based on

data acquired from the mediolateral diameter of the base of left metacarpal two

(sex classification accuracy of 91%). These results follow the trend that measures

of width and breadth are more sexually dimorphic than measures of length in both

the fleshed and skeletal hand. It was concluded that metacarpal morphometric

data could be used to accurately estimate the sex of Spanish individuals.

vi) Case and Ross (2007)

The aim of this study was to produce discriminant functions for sex estimation

with length as the only variable. The maximum axial length of the metacarpals and

phalanges of both hands were acquired in in a total of 259 subjects (123 females

and 136 males) of Caucasian American or Caucasian European ancestry. The male

sample ranged in age from 18 to 60 years; for the female sample the range was 27

to 72 years.

Sex was estimated more accurately in the left (85.7%) compared to the right

(84.3%) hand. Case and Ross (2007) concluded that discriminant functions based

on length measurements alone could accurately estimate sex and suggested that

lengths be used in preference to transverse measurements. Although previous

literature indicated that transverse measurements are the most dimorphic

dimensions in the hand, Case and Ross suggest that this is likely due to the impact

of functional loading on these dimensions. As length measurements have a

tendency to remain unaffected by functional loading due to “activity-‐variation”

(Case & Ross 2007, pp.269), discriminant functions based on length are less likely

to be affected by population and temporal differences.

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32

vii) El Morsi and Hawary (2012)

The aim of this study was to produce sex estimation predictive models for the hand

bones of individuals from an Egyptian population. A total of 100 Egyptian

individuals (50 male and 50 female) were x-‐rayed and the maximum lengths of the

five metacarpals and 14 phalanges were acquired. Student’s t-‐tests were used to

assess if there was any significant differences between male and female

measurements prior to performing multiple logistic regression analyses. All mean

male measurements were significantly larger than mean female measurements (p

≤ 0.05). A test was also conducted to compare the mean values obtained for the

right and left sides of the pooled sample; the first metacarpal in male subjects was

the only bone reported to have any significant bilateral variance. The bilateral

predictive models derived from the data acquired resulted in correct classification

accuracies of 94% for males and 88% for females. When predictive models of the

right hand alone were used, a classification accuracy of 88% was achieved for both

males and females. The predictive models from the left hand data, however,

correctly classified 90% and 88% of males and females respectively. The

predictive models based on the lengths of the first proximal phalanx, first distal

phalanx, metacarpal three and metacarpal four were the most reliable models for

the estimation of sex in the Egyptian population.

This study confirms that sex can be estimated from length measurements of the

metacarpals and phalanges from individuals of an Egyptian origin. The results

from this study also suggest morphometric data can be accurately acquired in

radiographs. This may be useful in a forensic context whereby both a forensic

anthropologist and forensic pathologists require access to an individual for

examination; x-‐rays are non-‐invasive and would allow for adherent soft tissue to

remain intact, or for skeletal elements to be examined remotely if required.

33

CHAPTER FOUR

Materials and Methods

4.1 Introduction

The primary objective of the present research thesis is to quantify the expression

and magnitude of sexual dimorphism in the metacarpals and phalanges. This

chapter, therefore, accordingly outlines the data collection methods and the

subsequent statistical analyses. The materials studied are digital antero-‐posterior

x-‐rays of the right hand. Validation of the data acquisition methods is also

considered in this chapter.

4.2 Materials

The individuals examined were separated into two sub-‐groups: an adult (300

individuals) and a sub-‐adult (100 individuals) sample. The adult sample comprises

digital hand x-‐rays of 150 males and 150 females; the age range for males was 18.3

to 64.3 years (mean 41.9) and for females was 18.5 to 68.4 years (mean 42.8). The

sub-‐adult sample comprises 100 digital x-‐rays of individuals between 13 and 18

years of age (Table 4.1). The sub-‐adult x-‐rays were sorted into three age groups:

Group A for 12 to 14 years of age; Group B for 14 to 16 years of age; and Group C

for 16 to 18 years of age. The sub-‐adult x-‐rays were grouped into these two-‐year

intervals to allow for more robust statistical analyses of the sub-‐adult data (as

analyses would be conducted on a larger sample than if data was grouped into one

year intervals) and to account for the stages of maturation that can occur at

variable ages between individuals. The sub-‐adult sample is used to quantify the

age at which the hand bones are sexually dimorphic and therefore the youngest

age at which sex estimation discriminant functions can be reliably applied to a


The digital hand x-‐rays analysed were acquired from a Picture Archiving and

Communication Systems (PACS) database; this contains medical scans from

various Western Australian hospitals and is maintained by the Department of

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34

Health (DOH). Radiographs of hands that showed little to no skeletal trauma in the

metacarpals and phalanges, and had no obvious anomalies (such as abnormal

osseous growths), were included in the study. Additional to the latter inclusion

requirements, the selected radiographs had to present clear identification of the

landmarks defining the required linear measurements.

Table 4.1 The number individuals in each age group in the sub-‐adult sample.

Age group Sex n

Group A Male 10

(12 – 14 years) Female 11

Group B Male 20


Group C Male 20


As this study was based on human subjects, approval was required from the

Human Research Ethics Committee (HREC) of the University of Western Australia.

Approval was granted on 11th October 2012 (Project No: RA/4/1/4362) and data

collection commenced thereafter.

4.3 Methods

The two methods evaluated for their suitability for the acquisition of linear

measurements in digital, as well as the definitions of the measurements

subsequently calculated, are outlined below. A precision study was first performed

to assess which of the two available methods for data acquisition provided the

most accurate and reliable data. The statistical approaches for the precision test,

and the subsequent analysis of sexual dimorphism in the hand bones, are outlined

below.

4.3.1 Landmark definitions and typology

i) Landmark definition

Landmarks, in an anatomical context, are “biologically meaningful” (Valeri et al.

1998) points that can be readily identified and are considered homologous

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35

between specimens (O’Higgins 2000; Richtmeier et al. 1995) The acquisition of

landmarks facilitates the analysis of variance in biological forms with landmark

data being “a representation of homologous structures” (Richtmeier et al. 1995,

pp.218). Bookstein (1991) defines three categories of landmarks commonly

recognised within morphometric analyses: Type I; Type II; and Type III.

Type I: These landmarks are considered to be points that can be readily identified

as their location is based on their immediate surroundings that are typically

recognisable anatomical features; such as the point at which different tissues,

structures or bones meet (Bookstein 1991; O’Higgins 2000; Ross & Williams

2008). Examples of Type I landmarks include anatomical features such as cranial

sutures (Coronal suture, Bregma, etc.) and blood vessel branches (Aorta) or

foramina (Foramen magnum) (Bookstein 1991; Valeri et al. 1998). Type I

landmarks are reproducible because they represent anatomical features that are

normally considered to be the same between specimens; for example, if the

bregma (the junction of the sagittal and coronal sutures) is used as a landmark,

then it must follow the same definition between all specimens in the study

(Cramon-‐Taubadel et al. 2007; O’Higgins 2000).

Type II: Type II and Type III landmarks tend to be more ambiguous to locate and

identify than Type I landmarks (Cramon-‐Taubadel et al. 2007). The Type II

landmarks category is the intermediate category, consisting of anatomical points

that are homologous between specimens based on their geometric relationship

with their immediate surroundings (Bookstein 1991; Ross & Williams 2008).

Bookstein (1991) refers to Type II landmarks as “maxima of curvature of other

local morphometric processes” (pp.64) and such landmarks include, for example,

points of muscle attachment (O’Higgins 2000; Ross & Williams 2008).

Type III: These landmarks are “extremal points” (Bookstein 1991, pp.65) that

points are most likely to be endpoints of an overall distance, or a point that is

defined as furthest away from another (Bookstein 1991; O’Higgins 2000). Type III

landmarks have at least one coordinate that is inconsistent (Bookstein 1991). This

means that the location of a landmark can, at most, be narrowed down to a border,

surface or end point; the landmark is therefore less likely to be reproducible or

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36

remain consistent between specimens (Valeri et al. 1998; Slice et al. 2004). The

medial point of an epiphysis, an end point of a diameter or location of a fissure are

all examples of Type III landmarks.

In the present study a total of 80 landmarks were acquired in each digital hand x-‐

ray; eight landmarks in each of the five metacarpals and five proximal phalanges.

The definitions of those landmarks are based on previously published papers – see

Figure 4.1 and Table 4.2, which outlines the landmarks acquired using metacarpal

two as an example. The landmarks acquired in this study are predominately Type

III landmarks (Table 4.2), which gives reason to expect relatively lower intra-‐

observer accordance and measurement accuracy. However, a precision test was

conducted prior to data acquisition in order to statistically quantify the accuracy

and reliability of landmark acquisition – see Section 4.5 below.

Figure 4.1 Antero-‐posterior view of metacarpal two, metacarpal one

and proximal phalanx one (from left to right) illustrating the eight

landmarks defined in Table 4.2.

4.3.2 Measurement definitions

The linear measurements acquired in this study largely follow established

definitions (Scheuer & Elkington 1993; Case & Ross 2007) and any modifications

are accordingly noted. There are four measurements taken in each of the five

metacarpals and proximal phalanges; each measurement corresponds to the linear

MDMC1

MPMC1

MHMC1 LHMC1

MBMC1 LBMC1

MSMC1 LSMC1

MDPP1

MHPP1

MPPP1

MBPP1

MSPP1

LHPP1

LBPP1

LSPP1

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37

distance between two pre-‐determined landmarks. Figure 4.2 and Table 4.3 outline

the measurements acquired using metacarpal two as an example.

Table 4.2 Definitions of the landmarks acquired for metacarpal two

Landmark Name Code Description

Most distal point of metacarpal twoi, ii

MDMC2 The point of metacarpal two that is furthest from the carpus when the hand is in anatomical position.

Most proximal point of metacarpal twoi, ii

MPMC2 The point of metacarpal two that is closest to the carpus when the hand is in anatomical position.

Most medial point of the head of metacarpal twoi, ii, iii

MHMC2 The point of the head that is closest to the 5th digit or mid-‐line of the body when the hand is in anatomical position.

Most lateral point of the head of metacarpal twoi, ii, iii

LHMC2 The point of the head that is on the side on the thumb side of the bone or furthest from the mid-‐line of the body when the hand is in anatomical position.

Most medial point of the base of metacarpal twoi, ii, iii

MBMC2 The point of the base that is on the side on the little finger side of the bone or closest the mid-‐line of the body when the hand is in anatomical position.

Most lateral point of the base of metacarpal twoi, ii, iii

LBMC2 The point of the base that is on the side on the thumb side of the bone or furthest from the mid-‐line of the body when the hand is in anatomical position.

Most medial point of the mid-‐shaft region of metacarpal twoi, ii, iii, iv

MSMC2 Point in the mid-‐shaft region that is on the side on the little finger side of the bone or closest the mid-‐line of the body when the hand is in anatomical position.

Most lateral point of the mid-‐shaft region of metacarpal twoi, ii, iii, iv

LSMC2 Point in the mid-‐shaft region that is on the side on the thumb side of the bone or furthest from the mid-‐line of the body when the hand is in anatomical position.

Key: i. Falsetti, 1995; ii. Smith, 1996; iii. Scheuer & Elkington, 1993; iv. Lazenby, 1994.

4.3.3 Measurement acquisition – OsiriX®

The medical image processing software OsiriX® offers a number of different

approaches to measurement acquisition; the validity of two approaches were

tested.

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38

Figure 4.2 Antero-‐posterior view of metacarpal two illustrating the

four measurements (See Table 4.3 for definitions)

Table 4.3 Definitions of acquired measurements for metacarpal two

Measurement name Code Definition #Landmarks

Maximum length of metacarpal twoi, ii, iii

MLMC2

The maximum linear distance between the most distal point of the bone to the most proximal point of the bone.

MPMC2 -‐ MDMC2

Mediolateral head width of metacarpal twoii, iii, iv, v

WHMC2

The maximum linear distance between the most medial point and the most lateral point of the head of the bone.

MHMC2 -‐ LHMC2

Mediolateral base width of metacarpal twoii, iii, iv, v

WBMC2

The maximum linear distance between the most medial point and the most lateral point of the base of the bone.

MBMC2 -‐ LBMC2

Mediolateral mid-‐shaft width of metacarpal twoii, iii, iv, v

WMMC2 The maximum linear distance between the most medial and most lateral point of the mid-‐shaft region.

MSMC2 -‐ LSMC2

Key: i. Case & Ross, 2007; ii. Falsetti, 1995; iii. Smith, 1996; iv. Scheuer & Elkington, 1993; v.

Lazenby, 1994. # Landmarks defined in Table 4.2

MLMC2

WHMC2

WMMC2

WBMC2

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39

i) Landmark acquisition

The landmark method involves identifying and recording the three-‐dimensional

(3D -‐ x, y, z) coordinates of landmarks that define a particular measurement in the

digital hand x-‐rays. The 3D coordinates of each landmark is exported from OsiriX®

(in a ‘csv’ format) into MorphDB which is a program used to calculate linear inter-‐

landmark distances. MorphDB is a Centre for Forensic Science (UWA) developed

programme that calculates linear measurements from x, y, z coordinate data

exported from OsiriX® and also produces data files suitable for direct import into

the Statistical Package for the Social Sciences (SPSS) software.

For example, with reference to metacarpal two, maximum length (MLMC2) is

defined as the distance between the most distal and proximal points (MDMC2 to

MPMC2). In this instance, MorphDB would calculate the inter-‐landmark distance

between the 3D coordinates of the MDMC2 and MPMC2 landmarks resulting in a

value for MLMC2. The landmark method required 80 landmarks to be located,

labelled and imported into MorphDB for each digital hand x-‐rays.

ii) Measurement acquisition

The line-‐tool method is a standard inclusion in the software (OsiriX®) that allows

direct measurements in any medical modality. A line is drawn between the

landmarks that define any measurement; the position of either landmark can be

moved medio-‐laterally to find the maximum length or in a proximal/distal

direction for a maximum width. The line-‐tool method was used to directly acquire

a total of 40 measurements in each digital hand x-‐ray.

4.4 Statistical analyses: precision test

Prior to data collection a precision study was performed to statistically quantify

the intra-‐observer error and thus determine data quality. In general there are two

main sources of measurement error; human error or “intra-‐individual variation”

(Liu 1988) and error resulting from inaccurate measuring equipment (Liu 1988;

Goto & Mascie-‐Taylor 2007). It is important to conduct a precision study prior to

data collection, as sources of error need to be quantified. High percentages of

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40

measurement error can affect statistical validity, as variation may be more

representative of measurement error rather than the genetic and environmental

factors it is meant to represent (Weinberg et al. 2005; Franklin et al. 2012a). A

precision study also allows for measurements to be re-‐defined, or methods of data

collection to be altered, if they are found to be inaccurate and unreliable.

To quantify measurement error, six randomly selected hand x-‐rays were measured

a total of six times each, with a minimum of 1 day between repeats to minimise

data recall. As there is a potential for the current study to produce population

specific standards that can be used for further forensic applications, the precision

test was practiced with caution. A six by six precision test format was chosen to

ensure statistics quantifying intra-‐observer error were based on reliable data.

Measurements were acquired using both measurement methods (see above); the

technical error of measurement (TEM), the relative technical error of

measurement (rTEM) and the coefficient of reliability (R) were then calculated.

These statistics are accordingly defined below.

i) Technical error of measurement

The technical error measurement (TEM) provides a measure of “the magnitude of

error” (Weinberg et al. 2005, pp.369) and is presented in the units of the original

measurement. This is used to estimate any intra-‐ or inter-‐observer precision by

measuring the standard deviation between repeated measurements (Goto &

Mascie-‐Taylor 2007). The TEM calculated by considering the difference between

the measurements taken along with the number of measurements and the number

replicates. TEM is considered to be an “accuracy index” (Goto & Mascie-‐Taylor

2007, pp.254) and is an indication of how much variation in a trait can be

attributed to observer error, rather than genetic or environmental factors. The

𝑇𝐸𝑀 is calculated as:

TEM = √(∑D)2

xN

Key: D: difference between measurements

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41

N: number of replicates

x : number of measurements taken or number of subjects

ii) Relative technical of error of measurement

The relative technical error of measurement is a standardisation of the TEM where

the TEM is expressed as a percentage (Goto & Mascie-‐Taylor 2007; Arroyo et al.

2010). This allows comparison between measurements of a different size, as TEM

is positively related to measurement size; e.g. larger measurements (such as

maximum length) having a larger TEM. Previous studies (e.g. Ulijaszek & Kerr

1999; Goto & Mascie-‐Taylor 2007) have suggested that an rTEM value higher than

5% is an indicator of “imprecise” data collection. The formula for the calculation of

rTEM is:

rTEM = TEM

x100 VAV

Key: VAV: variable average value

iii) Coefficient of reliability

The coefficient of reliability is an estimation of how much variation is not

attributable to measurement error (Weinberg et al. 2005). The value of ‘R’ ranges

from 0 to 1; a value of 0 (or closer to 0) would suggest that any between subject

measurement variation is most likely due to measurement error (Marks et al.

1989). A value of 1 (or close to 1), therefore, indicates that any variation found

between subjects is not present due to measurement error (Marks et al. 1989). For

example, an ‘R’ of 0.95 suggests that 95% of any variation is a result of genetic or

environmental factors rather than observer error. This leaves 5% variation

accounted for by observer (or measurement) error, thus suggesting that only 5%

of measurement variation is due to imprecision. Ulijaszek and Kerr (1999) suggest

that a coefficient of reliability of 0.9 (or higher) is required for data to be

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42

considered accurate and reliable (Weinberg et al. 2005; Franklin et al. 2012a;

Marks et al. 1989).

The coefficient of reliability formula is:

R = total TEM2

SD2

Key: SD2 – population variation of the trait that was measured.

4.5 Statistical analyses: measurement data

Prior to subsequent statistical analyses, descriptive statistics such as the mean,

standard deviation and range are first calculated. Normality was also tested using

the Shapiro-‐Wilk test, which is discussed below. Further statistical analyses were

conducted to assess the significance of difference in measurement data between

males and females, as well as the relationship between sex and the linear

measurements. All statistical analyses were performed using the Statistical Package

for the Social Sciences (SPSS) version 19.0.

4.5.1 Normality tests

In order to perform analyses such as an ANOVA or discriminant function analysis,

it is recommended that the data are normally distributed. The SPSS software offers

two tests of normality; the Kolmogorov-‐Smirnov and Shapiro-‐Wilk tests. Both are

applicable to the data acquired, however, the Shapiro-‐Wilk test is considered to be

more robust for data sets with less than 2000 samples. It is for the latter reason

that this test was used instead of the Kolmogorov-‐Smirnov statistic (Shapiro, Wilk

& Chen 1968). The Shapiro-‐Wilk statistic standardises the test data and compares

it to a normal distribution. The variance between the two distributions is reported,

along with a p-‐value indicating statistical significance. The Shapiro-‐Wilk test

assumes that the data is from a normal distribution as its null hypothesis.

Therefore, if the p-‐value reported is statistically significant (p<0.05), the null

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43

hypothesis is rejected, and that measurement violates assumptions of a normal

distribution.

4.5.2 Significance tests

i) Independent sample t-‐test

An independent sample t-‐test is used to assess the differences in the means of a

dependent variable between two samples from different populations. For this

particular study, the mean hand bone measurements acquired from the males and

females sampled from the Western Australian population are compared to the

mean measurements acquired from five foreign populations. In addition, the mean

hand bone measurements of males and females within the same population are

also compared using a t-‐test and the magnitude of differences is compared

between five comparative populations. As the comparison is performed using data

from previously published papers, independent t-‐tests will be calculated using the

mean, standard deviation and n values (sample size) as the raw measurement data

is not presented in the published literature.

The null hypothesis is that there is no significant difference between the two

sample means and any variance is due to factors other than the independent

variable (sex) (Lucy 2005; Townend 2003). The alternate hypothesis, therefore, is

that there is a significant difference between sample means (hand bone

measurements) and the variance may be due to a sex difference. From a t-‐test a p-‐

value is obtained which indicates the likelihood the means of the samples vary due

factors other than the independent variable; i.e. chance or measurement error

(Madrigal 2012; Lucy 2005). A p-‐value greater than 0.05 suggests that there is no

significant difference between the samples, as the evident variance is likely related

to factors other than sex 5% of the time or higher (Madrigal 2012; Townend 2003).

A p-‐value of less than 0.05 is associated with a significant difference between two

populations.

ii) Analysis of variance (ANOVA)

An ANOVA, like an independent sample t-‐test, compares the means of two samples

to determine if there is a significant difference (DeVeaux et al. 2012; Madrigal

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44

2012). The ANOVA expresses the dispersion of sample means around the mean of

all observations (e.g. males and females combined, or the total data set.) (Lucy

2005). An ANOVA, however, generally provides a more robust statistical analysis

than an independent sample t-‐test, due to an additional statistics -‐ the F-‐ratio. The

F-‐ratio expresses the dependence of a variable on the factors the study is based on

(Madrigal 2012). For this study, the F-‐ratio would indicate how dependent the

variance in measurement data between the male and female sample is on the sex

difference between the groups. The F-‐ratio is calculated by comparing the variance

within a sample with the variance between the samples (Madrigal 2012). The

equation is as follows;

F = between sample variance within sample variance

A high F-‐value thus suggests that the ‘between sample’ variance is greater than the

‘within sample’ variance. A p-‐value is also calculated and is used to assess whether

the variance between means (and thus the high F-‐score) is likely to have occurred

due to error or an independent variable (Ramsey & Schafer 2002). F-‐ratios also

allow for the identification of measurements that are more likely to express sexual

dimorphism (Franklin et al. 2008). This assists in interpreting which

measurements of the hand bones are likely to be the most accurate for estimating

sex.

In the present study, a one-‐way ANOVA is used with sex as the dependent variable

and the hand bone measurements as the independent variables. The null

hypothesis of the one-‐way ANOVA model is that sample means are identical and

imply that there is no significant difference between male and female

measurement values (Lucy 2005).

4.5.3 Discriminant function analyses

Discriminant function analysis (DFA) involves establishing a function that includes

a combination of variables for discriminating between two (or more) groups

(Agresti 2002; Slaus & Tomicic 2005; Pietrusewsky 2008). A DFA allows for the

formulation of a prediction model that, in reference to the present study, facilitates

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45

sex estimation based on hand bones (Patriquin et al. 2005; Slaus & Tomicic 2005).

This statistic allocates group membership and also provides an indication of the

strength of a relationship that a dependent variable has with the independent

variable; the variables most likely to accurately predict group membership are

more heavily weighted in the discriminant function (e.g. higher unstandardized

coefficient values) (Işcan et al.1998).

The discriminant scores produced from a discriminant function are classified

based on their relationship to a sectioning point. The sectioning point of this study,

as per previously published studies, is established as a value that is halfway

between the male and female mean scores (Slaus & Tomicic 2005). If the

discriminant score is lower than the sectioning point, the discriminant score is

likely to represent a female individual (Agresti 2002). Two DFA approaches were

applied and cross-‐validation for both methods was conducted to ensure the

validity of the predictive models produced. The cross-‐validation gives an estimate

of how accurately a function will allocate group membership by testing the model

on a variable that was leave out of the sample during the discriminant function

analysis (Agresti 2002; Patriquin et al. 2005). Posterior probabilities are calculated

to also assess how accurate and effective a predictive model is in classifying

variables. A score is given between 0 and 1, with scores closer to one suggesting

that a variable is further away from the sectioning point than variables with scores

closer to 0. If a variable is further from the sectioning point, the associated function

the likelihood of classification due to chance decreases. This renders the function

effective.

i) Direct discriminant function analysis

Direct DFA is the production of a predictive model based on the individual needs of

the user, with variables in the discriminant function manually selected. Direct DFA

would be considered in situations such as those published by Stojanowski (1999),

who formulated seven discriminant functions designed for the application in

metacarpals in different stages of preservation. Direct DFA essentially allows for

the manipulation of a predictive model based on what data is available. The

predictive models used as a result of direct DFA are not necessarily the most

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46

accurate; this is because the variables inputted may not be the most accurate

predictors of group membership. In some instances, a discriminant function using

only one variable is required and with these cases a demarking point is calculated

(Patriquin et al. 2005). The demarking point is essentially the sectioning point for

the discriminant scores using the single variable discriminant function and is the

halfway point between the combined male and female mean measurement value

for that variable (Franklin et al. 2008).

ii) Stepwise discriminant function analysis

Stepwise DFA is a method where the discriminant function is generated

sequentially through a method of variable selection or elimination (Ramsey &

Schafer 2002; Agresti 2002). A stepwise DFA generally results in functions that

provide the highest classification accuracy as the independent variables with the

highest F-‐vales are selected (Işcan et al. 1998). Inclusion of variables in the

predictive model is based on the Wilk’s lambda statistic; this is a measure of how

much variance in discriminant scores cannot be attributed to the differences that

exist between the groups considered (Ramsey & Schafer 2002; Agresti 2002). A

lower Wilk’s lambda value would, therefore, suggest that there is a lower

percentage of variance that can be explained by something other than the

difference between groups. Independent variables that result in a lower Wilk’s

lambda value are included in the predictive model due to their existing

relationship with the dependent variable. A low Wilk’s lambda value, however,

does not necessarily suggest that a predictive model has high classification

accuracy (Ramsey & Schafer 2002). Rather, the classification accuracy of

discriminant functions developed will be calculated as the percentage of subjects

from the data sample that can be correctly classified using the developed

discriminant functions.

iii) Testing of foreign discriminant function analyses

Previously published predictive models based on populations foreign to Western

Australia were applied to the Western Australian data acquired. This was to

further establish the need for population specific sex estimations standards. From

each of the comparative populations chosen, the function that had the highest

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47

classification accuracy and measurements in common with the current study was

used to classify the individual sample.

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48

49

CHAPTER FIVE

Results

5.1 Introduction

This chapter outlines the results from each of the statistical analyses conducted in

this study, including the precision test, comparison of mean male and female data,

the assessment of population differences, and the results of the various

discriminant function analyses. The dataset used in this study consisted of two

groups; adults (150 males and 150 females) and sub-‐adults (50 males and 50

females). The groups were analysed separately in order to fulfil the required aims

of the study (see Chapter One). The results from the precision test are presented

first, followed by the results from the analysis of the adult, and then the sub-‐adult,

data.

5.2 Measurement precision

Two measurement methods (landmark and line-‐tool -‐ see Chapter Four) were

evaluated prior to data acquisition to assess which produced the most accurate

and reliable data. Measurement precision was quantified using the technical and

relative technical error of measurement (TEM; rTEM) and the coefficient of

reliability (R) (Table 5.1 and Table 5.2).

i) Landmark method

The results from the precision test for the landmark method are shown in Table

5,1. The rTEM values ranged from 0.49% (maximum length of metacarpal three) to

3.26% (maximum mid-‐shaft width of proximal phalanx one). The mean rTEM was

1.77%. The lowest R-‐value was 0.96 for the maximum mid-‐shaft width of proximal

phalanx one. The highest R-‐value of 1.00 was shared by 12 measurements (Table

5.1). The mean R-‐value for measurements acquired using the landmark method

was 0.99.

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50

Table 5.1 Measurement precision (TEM, rTEM and R) for the landmark measurement method. Measurement TEM R rTEM MC1 Maximum Length (MLMC1) 3.05 1.00 0.76 MC1 Mediolateral Width Head (WHMC1) 2.20 0.99 1.70 MC1 Mediolateral Width Base (WBMC1) 3.07 0.99 2.31 MC1 Maximum Mid-‐Shaft Width (WMMC1) 1.97 0.99 2.42 MC2 Maximum length (MLMC2) 3.16 1.00 0.51 MC2 Mediolateral Width Head (WHMC2) 3.18 0.99 2.43 MC2 Mediolateral Width Base (WBMC2) 4.19 0.98 2.61 MC2 Maximum Mid-‐shaft Width (WMMC2) 2.01 0.98 2.58 MC3 Maximum length (MLMC3) 2.84 1.00 0.49 MC3 Mediolateral Width Head (WHMC3) 2.53 0.99 1.86 MC3 Mediolateral Width Base (WBMC3) 3.23 0.98 2.66 MC3 Maximum Mid-‐shaft Width (WMMC3) 1.09 0.99 1.47 MC4 Maximum length (MLMC4) 3.59 1.00 0.70 MC4 Mediolateral Width Head (WHMC4) 2.95 0.99 2.50 MC4 Mediolateral Width Base (WBMC4) 2.62 0.99 2.37 MC4 Maximum Mid-‐shaft Width (WMMC4) 1.34 0.99 2.21 MC5 Maximum length (MLMC5) 3.62 1.00 0.77 MC5 Mediolateral Width Head (WHMC5) 1.89 0.99 1.61 MC5 Mediolateral Width Base (WBMC5) 3.08 0.97 2.59 MC5 Maximum Mid-‐shaft Width (WMMC5) 1.74 0.98 2.55 PP1 Maximum Length (MLPP1) 2.16 1.00 0.77 PP1 Mediolateral Width Head (WHPP1) 2.21 0.98 2.27 PP1 Mediolateral Width Base (WBPP1) 2.84 0.98 2.29 PP1 Maximum Mid-‐shaft Width (WMPP1) 2.14 0.96 3.26 PP2 Maximum Length (MLPP2) 2.33 1.00 0.64 PP2 Mediolateral Width Head (WHPP2) 2.08 0.98 2.17 PP2 Mediolateral Width Base (WBPP2) 1.62 1.00 1.15 PP2 Maximum Mid-‐shaft Width (WMPP2) 1.59 0.99 1.99 PP3 Maximum Length (MLPP3) 2.87 1.00 0.71 PP3 Mediolateral Width Head (WHPP3) 2.54 0.98 2.42 PP3 Mediolateral Width Base (WBPP3) 1.77 1.00 1.28 PP3 Maximum Mid-‐shaft Width (WMPP3) 1.52 0.99 1.84 PP4 Maximum Length (MLPP4) 2.23 1.00 0.58 PP4 Mediolateral Width Head (WHPP4) 1.33 1.00 1.34 PP4 Mediolateral Width Base (WBPP4) 1.77 0.99 1.36 PP4 Maximum Mid-‐shaft Width (WMPP4) 1.52 0.99 1.95 PP5 Maximum Length (MLPP5) 1.95 1.00 0.65 PP5 Mediolateral Width Head (WHPP5) 2.08 0.98 2.53 PP5 Mediolateral Width Base (WBPP5) 2.31 0.99 1.87 PP5 Maximum Mid-‐shaft Width (WMPP5) 1.67 0.98 2.53

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Table 5.2 Measurement precision (TEM, rTEM and R) for the line-‐tool measurement method. Measurement TEM R rTEM MC1 Maximum Length (MLMC1) 2.54 1.00 0.63 MC1 Mediolateral Width Head (WHMC1) 2.48 0.99 1.87 MC1 Mediolateral Width Base (WBMC1) 1.36 1.00 1.02 MC1 Maximum Mid-‐Shaft Width (WMMC1) 1.44 0.99 1.81 MC2 Maximum length (MLMC2) 1.94 1.00 0.31 MC2 Mediolateral Width Head (WHMC2) 2.30 0.99 1.73 MC2 Mediolateral Width Base (WBMC2) 2.08 1.00 1.29 MC2 Maximum Mid-‐shaft Width (WMMC2) 2.11 0.98 2.79 MC3 Maximum length (MLMC3) 1.40 1.00 0.24 MC3 Mediolateral Width Head (WHMC3) 1.95 1.00 1.41 MC3 Mediolateral Width Base (WBMC3) 2.99 0.98 2.40 MC3 Maximum Mid-‐shaft Width (WMMC3) 2.48 0.97 3.34 MC4 Maximum length (MLMC4) 1.96 1.00 0.38 MC4 Mediolateral Width Head (WHMC4) 1.35 1.00 1.11 MC4 Mediolateral Width Base (WBMC4) 2.34 0.99 2.02 MC4 Maximum Mid-‐shaft Width (WMMC4) 0.92 0.99 1.54 MC5 Maximum length (MLMC5) 2.45 1.00 0.52 MC5 Mediolateral Width Head (WHMC5) 1.06 1.00 0.89 MC5 Mediolateral Width Base (WBMC5) 1.37 0.99 1.14 MC5 Maximum Mid-‐shaft Width (WMMC5) 1.47 0.98 2.12 PP1 Maximum Length (MLPP1) 1.38 1.00 0.49 PP1 Mediolateral Width Head (WHPP1) 1.55 0.99 1.57 PP1 Mediolateral Width Base (WBPP1) 1.55 0.99 1.23 PP1 Maximum Mid-‐shaft Width (WMPP1) 1.67 0.98 2.59 PP2 Maximum Length (MLPP2) 1.37 1.00 0.38 PP2 Mediolateral Width Head (WHPP2) 1.60 0.99 1.61 PP2 Mediolateral Width Base (WBPP2) 1.17 1.00 0.82 PP2 Maximum Mid-‐shaft Width (WMPP2) 0.98 1.00 1.24 PP3 Maximum Length (MLPP3) 1.67 1.00 0.41 PP3 Mediolateral Width Head (WHPP3) 1.60 0.99 1.48 PP3 Mediolateral Width Base (WBPP3) 1.07 1.00 0.76 PP3 Maximum Mid-‐shaft Width (WMPP3) 1.10 1.00 1.33 PP4 Maximum Length (MLPP4) 1.26 1.00 0.33 PP4 Mediolateral Width Head (WHPP4) 1.48 0.99 1.46 PP4 Mediolateral Width Base (WBPP4) 1.34 1.00 1.02 PP4 Maximum Mid-‐shaft Width (WMPP4) 1.07 1.00 1.39 PP5 Maximum Length (MLPP5) 1.26 1.00 0.42 PP5 Mediolateral Width Head (WHPP5) 1.94 0.98 2.33 PP5 Mediolateral Width Base (WBPP5) 1.38 1.00 1.12 PP5 Maximum Mid-‐shaft Width (WMPP5) 1.81 0.98 2.75

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ii) Line-‐tool method

The results of the precision test for the line-‐tool method are shown in Table 5.2.

The lowest rTEM (0.24%) was for the maximum length of metacarpal three. The

highest rTEM (3.34%) was for the maximum mid-‐shaft width of metacarpal four.

The mean rTEM was 1.33%. The R-‐values for the line-‐tool method measurements

from 0.97 (maximum mid-‐shaft width of metacarpal three) to 1.00; the highest R-‐

value is shared by 20 measurements (Table 5.2).

iii) Summary

Both measurement methods yielded results with rTEM values on average much

less than 5% and R-‐values that were equal (or close) to 1. Overall, the line-‐tool

method is the most precise and reproducible of the two methods examined (lower

mean rTEM and higher R-‐values). For this reason, subsequent data collection

conducted was performed using the line-‐tool measurement method.

5.3 Descriptive statistics for the adult data

5.3.1 Age distribution

The mean age, standard deviation and range for the male and female individuals

are shown in Table 5.3. The adult males ranged from 18.34 to 64.34 years of age

(mean 41.94 years). The age range for the adult females was from 18.00 to 68.36

(mean 42.26 years).

Table 5.3 Distribution of age (in years) of the adult Western Australian sample.

Sex n Range Mean Standard Deviation

Male 150 18.34 – 64.34 41.94 14.11

Female 150 18.00 – 68.36 42.26 14.19

5.3.2 Measurement Normality

Measurement normality was tested using the Shapiro-‐Wilk method; the data is

standardised and compared to a normal distribution. Of the 40 measurements

acquired in the male sample, a total of 37 were normally distributed. The three

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male measurements that violate assumptions of normality are the base width of

metacarpal one, base width of metacarpal three and the head width of metacarpal

five. For the female sample, a total of two measurements were not normally

distributed; the base width of metacarpal one and, the head width of proximal

phalanx two.

Inspection of the raw data for these five measurements indicated no outliers

outside of three standard deviations of the mean. As ANOVA and discriminant

function analyses are relatively robust to violations of normality, the five

measurements were retained (Ramsey and Schafer 2002; DeVeaux et al. 2012).

Subsequent analyses performed using these measurements were duly scrutinised

to ensure their robustness to prevent potentially erroneous results.

5.3.3 Univariate comparisons

The descriptive statistics for the adult hand bone measurements are presented in

Table 5.4. Overall, the mean male values were larger than those of the female

individuals for all measurements. The maximum length of metacarpal three had

the largest mean sex difference (5.77 mm) and maximum mid-‐shaft width of

metacarpal three had the smallest mean sex difference (1.07 mm). In general the

maximum length and base width measurements had larger mean differences

between the sexes, compared to the head and mid-‐shaft width measurements

(Table 5.4).

To assess the extent of morphometric sexual dimorphism, an ANOVA was

conducted to compare the adult male and female data. All measurements were

statistically significantly different (Table 5.4). The most sexually dimorphic

measurement was the base width of proximal phalanx two (F=363.88; p<0.001).

The least sexually dimorphic measurement was the maximum length of metacarpal

five (F=61.35; P<0.001). Sex differences explain 17 – 55% of sample variance.

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Table 5.4 Descriptive statistics and means comparison of mean hand bone measurements (in mm).

Measurement a Male (n = 150) Female (n=150) F R-‐square p-‐value

Mean SD Range Mean SD Range MLMC1 48.86 3.21 40.50 – 57.81 44.6 2.88 36.72 – 51.90 146.13 0.33 *** WHMC1 17.03 1.41 12.97 – 20.73 14.92 1.11 11.81 – 17.64 207.59 0.32 *** WBMC1 16.61 1.6 13.76 – 21.77 14.7 1.2 12.69 – 18.14 137.31 0.41 *** WMMC1 9.91 1 7.38 – 13.03 8.78 0.76 6.83 – 10.81 121.6 0.29 *** MLMC2 74.75 4.55 62.66 – 88.17 68.98 3.96 60.42 – 78.40 137.62 0.32 *** WHMC2 21.09 1.58 14.11 – 20.23 18.55 1.27 11.93 – 17.98 234.42 0.33 *** WBMC2 17.12 1.35 17.56 – 25.36 15.32 1.2 15.09 – 22.03 147.63 0.44 *** WMMC2 9.45 0.82 7.68 – 11.89 8.23 0.7 6.64 – 10.46 191.69 0.39 *** MLMC3 69.16 4.4 57.26 – 82.90 63.83 3.65 55.37 – 74.86 130.23 0.3 *** WHMC3 15.83 1.35 14.38 – 21.27 13.98 1.18 11.20 – 17.11 160.04 0.39 *** WBMC3 17.38 1.32 13.52 – 20.06 15.4 1.17 12.39 – 18.56 189.27 0.35 *** WMMC3 9.05 0.71 7.41 – 10.88 8.04 0.64 6.36 – 10.00 169.59 0.36 *** MLMC4 61.56 4.39 50.46 – 75.21 56.75 3.37 49.45 – 64.91 113.25 0.28 *** WHMC4 14.25 1.25 12.06 – 19.24 12.63 1.02 10.69 – 15.55 151.46 0.35 *** WBMC4 14.92 1.3 11.41 – 17.61 13.19 1.04 9.92 – 15.19 159.9 0.34 *** WMMC4 7.53 0.73 5.49 – 9.28 6.5 0.57 5.24 – 8.21 183.1 0.38 *** MLMC5 56.23 5.68 46.28 – 68.80 51.95 3.53 35.77 – 60.70 61.35 0.17 *** WHMC5 15.3 1.28 11.79 – 18.37 13.42 1.05 10.13 – 15.23 194.78 0.38 *** WBMC5 14.83 1.3 12.26 – 18.51 12.99 1.05 11.07 – 17.05 181.03 0.4 *** WMMC5 8.74 0.93 6.97 – 11.20 7.45 0.73 5.71 – 9.45 179.56 0.38 *** MLPP1 34.38 2.31 29.53 – 39.86 31.55 2.14 26.80 – 37.24 120.85 0.29 ***

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Measurement a Male (n = 150) Female (n=150) F R-‐square p-‐value

Mean SD Range Mean SD Range WHPP1 15.41 1.23 8.56 – 15.73 13.66 0.97 7.19 – 13.84 186.55 0.3 *** WBPP1 12.27 1.25 11.90 – 19.09 10.79 0.99 10.01 – 15.62 129.26 0.38 *** WMPP1 8.27 0.91 5.98 – 10.75 7.15 0.81 4.98 – 8.92 128.01 0.3 *** MLPP2 43.3 2.47 37.42 – 49.53 40.52 2.49 34.64 – 47.65 93.84 0.24 *** WHPP2 17.89 1.02 9.68 – 15.44 15.8 0.87 9.62 – 13.05 363.88 0.39 *** WBPP2 12.46 0.94 15.41 – 20.96 11.15 0.68 13.70 – 18.01 191.18 0.55 *** WMPP2 10.37 0.78 7.95 – 12.42 9.08 0.68 7.27 – 11.11 233.75 0.44 *** MLPP3 48.24 2.99 40.68 – 56.09 44.85 2.61 38.96 – 52.13 109.6 0.27 *** WHPP3 17.55 1.11 10.67 – 16.10 15.4 0.86 10.22 – 13.62 353.43 0.41 *** WBPP3 13.23 0.97 15.15 – 20.16 11.78 0.75 13.34 – 17.85 210.08 0.54 *** WMPP3 10.61 0.86 8.56 – 12.57 9.1 0.73 7.11 – 10.71 268.07 0.33 *** MLPP4 45.36 2.85 37.91 – 52.40 41.68 2.44 35.68 – 48.19 158.22 0.33 *** WHPP4 16.23 1.08 10.19 – 15.37 14.32 0.93 9.35 – 12.88 268.68 0.42 *** WBPP4 12.35 0.88 13.87 – 19.58 10.98 0.72 12.19 – 16.43 214.88 0.47 *** WMPP4 9.94 0.86 7.58 – 12.22 8.36 0.72 6.21 – 10.13 297.82 0.5 *** MLPP5 36.05 2.16 29.04 – 42.20 32.82 2.16 27.89 – 39.77 158.22 0.35 *** WHPP5 15.31 0.99 8.15 – 12.70 13.45 0.82 7.54 – 11.52 314.58 0.29 *** WBPP5 10.38 0.84 13.27 – 18.88 9.35 0.75 11.50 – 16.13 124.17 0.51 *** WMPP5 8.53 0.84 6.41 – 11.07 7.16 0.74 5.16 – 8.97 223.08 0.43 ***

Key: a Definition of measurements in Table 5.1; NS = not significant; *P<0.05, **P<0.01, ***P<0.001

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5.3.4 Discriminant function analyses

Discriminant function analyses were performed using different measurement

combinations. As it is possible that a complete hand may not always be available

for assessment in a forensic or archaeological context, demarking points were

calculated for each individual hand bone measurement; only the most accurate

function for each bone is reported. Thereafter, a series of direct multiple variable

and stepwise discriminant analyses were performed using measurements acquired

from the metacarpals alone, as these bones are more likely to be recovered in a

forensic or archaeological context.

i) Direct single variable functions

Demarking points were calculated for each of the 40 measurements acquired in

the hand to assess whether sex could be accurately estimated using a single

measurement. The combined cross-‐validated accuracy for the most accurate

variable for each bone is reported in Table 5.5, and ranged from 76.70 (Function 4;

WMMC4) to 85.70% (Function 7; WBPP2). Function 10 (requiring the base width

of proximal phalanx five WBPP5) is considered to be the most accurate variable for

estimating sex, as it had both a combined cross-‐validated accuracy above 80%

(85.30%) and a sex bias below 5% (-‐4.00%). Of the ten most accurate functions,

eight involve base width measurements.

ii) Direct multiple variable functions

A series of direct multiple variable discriminant analyses were performed for the

combined metacarpal measurements. There were only two metacarpals that

yielded accuracy above 80%; metacarpals two and five (Table 5.6). Functions 11

and 12 have classification accuracies of 84.00% and 84.30% respectively.

However, Function 11 should be used in preference to Function 12, as it has a

considerably smaller sex bias (-‐4.00% compared to -‐8.70%).

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Table 5.5 Direct single variable discriminant analyses of individual hand bones, including demarking point values (in mm).

Measurement# Demarking

points

Combined cross-‐

validated accuracy Sex bias

Metacarpal

Function 1. WBMC1 ♀ <15.98< ♂ 81.70% -‐ 2.00%

Function 2. WBMC2 ♀ <19.82< ♂ 81.30% -‐ 4.00%

Function 3. WHMC3 ♀ <16.22< ♂ 81.30% -‐ 6.70%

Function 4. WMMC4 ♀ < 7.02< ♂ 76.70% -‐ 6.70%

Function 5. WBMC5 ♀ <14.36< ♂ 80.30% -‐ 7.30%

Proximal Phalanx

Function 6. WBPP1 ♀ <14.53< ♂ 78.30% -‐ 0.70%

Function 7. WBPP2 ♀ <16.84< ♂ 85.70% -‐ 6.00%

Function 8. WBPP3 ♀ <16.48< ♂ 87.00% -‐ 7.40%

Function 9. WBPP4 ♀ <15.27< ♂ 81.30% -‐ 4.00%

Function 10. WBPP5 ♀ <14.38< ♂ 85.30% -‐ 4.00%

Key: #Definition of measurements in Table 5.1; ♂ = Male, ♀ = Female

Table 5.6 Direct multiple variable discriminant analysis of metacarpals.

#Equation: unstandardised coefficients and constant

Group centroids &

sectioning point

Correctly assigned

Sex bias

Function 11. Metacarpal Two

(0.41 x MLMC2) + (0.315 x WBMC2) + (0.181 x

WHMC2) + (0.567 x WMMC2) -‐17.108

♂ 1.024 ♂ 123/150; -‐ 4.00% 0.00 ♀ 129/150

♀ 1.024 [84.0%]

Function 12. Metacarpal Five

(0.037 x MLMC5) + (0.375 x WBMC5) + (0.244 x

WHMC5) + (0.525 x WMMC5) 15.005

♂ 0.994 ♂ 120/150; -‐ 8.70% 0.00 ♀ 133/150

♀ 0.994 [84.3%]


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iii) Stepwise discriminant analysis

A stepwise discriminant function analysis was performed, for the complete hand

(Function 13) and for each individual digit (Functions 14 to 18) (Table 5.7). The

stepwise discriminant function analysis of the complete hand selected eight

variables and achieved a cross-‐validated classification accuracy of 91.00% with a

sex bias of -‐6.00%. The stepwise discriminant function analysis of measurements

from each of the five digits resulted in classification accuracies from 79.70 to

87.70%. The highest sex classification accuracy was achieved for the fifth digit

using six variables (see Function 18; Table 5.7) with a cross-‐validated accuracy of

87.70% and a sex bias of -‐2.00% (Table 5.7).

5.3.5 Posterior probabilities

Posterior probability intervals for the 18 functions are provided in Appendix One

(Table A1.1). The overall percentage of individuals classified with a certainty of

above 80% for each of the direct discriminant functions (1-‐12) was less than that

calculated for the stepwise functions (13-‐18). Function 13 had the highest

percentage of correctly classified individuals within the ‘0.80-‐1.00’ interval for

both males (91.73%) and females (89.44%). The lowest percentage of individuals

classified with 80% and above certainty for males (54.17%) was calculated for

Function 1 and for females it was Function 3 (54.33%). There were no individuals

correctly classified in any discriminant function with less than 40% certainty.

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Table 5.7 Stepwise discriminant function analysis of the Western Australian adult sample.

Step #Variables Unstandardised coefficient

Standardised coefficient

Wilk’s lambda

Structure point

Group centroids

Sectioning point

Correctly assigned

Sex bias

Complete Hand

Function 13.

1 2 3 4 5 6 7 8

WBPP2 WMPP4 MLPP5 WHPP5 WBPP1 MLPP2 WBPP3 MLMC1 Constant

0.325 0.610 0.257 -‐0.590 0.190 -‐0.199 0.327 0.090 -‐18.107

0.308 0.486 0.572 -‐0.470 0.210 -‐0.495 0.325 0.275

0.450 0.411 0.395 0.378 0.369 0.360 0.352 0.346

0.803 0.727 0.530 0.469 0.575 0.408 0.792 0.509

♂ 1.371 ♀ -‐1.371

0.00 91.00% -‐6.00%

Individual Digits

Function 14.

1 2 3

WBMC1 MLMC1 WBPP1 Constant

0.400 0.112 0.336 -‐16.51

0.507 0.342 0.371

0.589 0.544 0.522

0.873 0.732 0.828

♂ 0.953 ♀ -‐0.953

0.00 79.70% -‐8.70%

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Wilk’s lambda

Structure point

Group centroids

Sectioning point

Correctly assigned

Sex bias

Function 15.

1 2

WBPP2 WMMC2 Constant

0.866 0.379 -‐17.94

0.823 0.289

0.450 0.434

0.968 0.703

♂ 1.137 ♀ -‐1.137

0.00 86.70% -‐8.00%

Function 16.

1 2 3

WBPP3 WMPP3 MLMC3 Constant

0.576 0.530 0.058 -‐18.56

0.573 0.423 0.233

0.457 0.429 0.418

0.923 0.804 0.561

♂ 1.175 ♀ -1.175

0.00 86.00% -‐5.40%

Function 17.

1 2 3

WMPP4 MLPP4 WBPP4 Constant

0.759 0.141 0.306 -‐17.74

0.604 0.373 0.310

0.500 0.441 0.427

0.864 0.601 0.820

♂ 1.154 ♀ -‐1.154

0.00 85.70% -‐6.00%

Function 18.

1 2 3 4

WBPP5 WMMC5 WBMC5 WMPP5

0.580 0.295 0.226 0.391

0.527 0.246 0.265 0.309

0.486 0.454 0.446 0.437

0.872 0.659 0.686 0.734

♂ 1.175 ♀ -‐1.175

0.00 87.70% -‐2.00%

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Wilk’s lambda

Structure point

Group centroids

Sectioning point

Correctly assigned

Sex bias

5 6

WHPP5 MLPP5 Constant

-‐0.425 0.113 -‐16.75

-‐0.338 0.251

0.429 0.419

0.548 0.618


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5.4 Population differences

5.4.1 Measurement differences

To evaluate the significance of metrical population variation the mean adult

metacarpal length measurements were compared to previously published data

from a selection of populations foreign to Australia (Table 5.8); the results are

outlined in Appendix Two (Table A2.1). Metacarpal length was the one

measurement common to the five comparative studies and was therefore

compared using a series of unpaired t-‐tests. The t-‐values reported for each of the

significance tests is an indication of the size of the difference between the two

means that were compared. A high positive t-‐value suggests that Western

Australian mean metacarpal length values are statistically significantly different

than their foreign counterparts.

Metacarpal length measurements were statistically significantly different for all

comparisons between Western Australian males and females and the comparative

populations, with the exception of the maximum length of metacarpal one between

Western Australian and Egyptian populations. The largest t-‐values, and thus

largest differences, were reported for the comparison of male mean metacarpal

one and two lengths between the Western Australian and the British populations

(t = 7.46 and t = 8.62 respectively). The largest t-‐value (t = 6.70) was found when

comparing the male mean for metacarpal four maximum lengths between Western

Australian and American populations. The lowest t-‐values, and therefore smallest

differences, were calculated when comparing Western Australian males to

Egyptian males for all three mean metacarpal length measurements.

A different trend was found when comparing the Western Australian female data

to the four comparative populations. Western Australian female mean values were

significantly larger than the Spanish population for metacarpal one (t = 5.34), two

(t = 10.14) and four (t = 7.01). Metacarpal one length values between the Western

Australian and Egyptian were not significant (t=0.09); corresponding p-‐value

indicating that these mean values were not statistically significantly different. In

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considering the lengths of metacarpal two and four, the Western Australian

females were most similar to the British population.

Table 5.8 Four comparative populations (including source of data and sample size)

#Caucasian or African American not stipulated

5.4.2 Variation in the expression of sexual dimorphism

The significance of sex differences in mean metacarpal within the Western

Australian and comparative populations were also evaluated using a series of

independent sample t-‐test. The results are presented in Appendix Three (Table

A3.1) and offer an indication of the magnitude of sexual dimorphism expressed by

the maximum length of metacarpals one, two and four for each five population.

All comparisons were found to be statistically significantly different for metacarpal

one; the largest difference was found when comparing male and female mean

values within the American population (t = 10.36; p <0.05). However, the largest

sex in metacarpal two and four was found in the Egyptian population (t = 11.96, p

<0.05 and t = 13.61, p <0.05 respectively). Overall, the British population had the

lowest level of sexual dimorphism in the three metacarpal lengths compared

(Appendix Three, Table A3.1). It is also evident that the magnitude of sexual

dimorphism expressed in the hand bones of Western Australian individuals was

most similar to the level of sexual dimorphism expressed in the British population

as demonstrated by the similar t-‐values reported.

Publication Population Males Females

Scheuer and Elkington (1993) Caucasian-‐British 33 27

Barrio et al. (2006) Spanish ancestry 36 36

Case and Ross (2007) Caucasian American and European

133 116

El Morsi and Hawary (2012) Egyptian Ancestry 100 100

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5.4.3 Classification accuracy

To further explore the need for population specific sex estimation standards,

discriminant functions for populations foreign to Western Australia were used to

classify individuals in the present study. The function that had measurements in

common with the current study, and the highest stated classification accuracy,

were used to classify the Western Australian sample. It was found that the

achieved classification accuracy is considerably lower than classification

accuracies originally reported (Table 5.9). The discriminant functions applied to

the current sample, excluding the North American discriminant function of Case

and Ross (2007), had a tendency to misclassify males as females. Conversely, the

Case and Ross (2007) function misclassified more than half the female sample as

male; overall the application of all the foreign functions resulted in extremely large

sex-‐bias values (Table 5.9).

Table 5.9 Classification accuracies when applying foreign standards to a Western Australian population

Reference Published Accuracy

Achieved Accuracy on the current population Sex Bias

Male Female Pooled

Scheuer and Elkington 1993

80.00% 5.33% [8/150] 100% [150/150] 52.66% -‐94.77%

Barrio et al. 2006

91.40% 0.00% [0/150] 100% [150/150] 50.00% -‐100%

Case and Ross 2007

83.10% 96.00% [144/150] 47.33% [71/150] 71.67% 48.67%

El Morsi and Hawary 2012

83.90% 2.67% [4/150] 68.00% [102/150] 35.34% -‐65.33%

The function that achieved the highest classification accuracy (71.67%) when

applied to the Western Australian population was that of Case and Ross (North

American) (2007). This function also achieved the smallest sex bias value of

48.67%. However, this considerably large sex bias renders the function

inapplicable to a Western Australian population, as female individuals are likely to

be misclassified as males. The largest sex bias value (-‐100%) was found when

using the Spanish function of Barrio et al. (2006). The lowest overall classification

accuracy was 35.34% when the Egyptian function of El Morsi and Hawary (2012)

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standard was applied. Overall, it is clearly evident that all four foreign standards

are completely unacceptable for application in a Western Australian population.

5.5 Sub-‐adult analyses

Another aim of the present study was to assess the age at which sex can be

accurately estimated in the sub-‐adult hand. A series of ANOVA’s were performed to

assess the significance of metric dimorphism at different ages: Group A (12-‐14

years); Group B (14-‐16 years); Group C (16-‐18 years). This was then followed by a

series of discriminant function analyses to quantify sex classification accuracy with

these age groups. The effect of classifying the sub-‐adults in each age group using an

adult discriminant function was also evaluated.

5.5.1 Age distribution

As discussed in Chapter Four, the sub-‐adult sample was split into three groups to

quantify the age at which the hand bones are significantly sexually dimorphic. The

mean, range and standard deviation for each of the three age groups is outlined in

Table 5.10.

Table 5.10 Distribution of age (in years) for each of the sex-‐specific sub-‐adult groups.

Age group Sex n Range Mean Standard Deviation

Group A Male 10 13.10 – 13.80 13.52 0.30

(12 – 14 years) Female 11 12.70 – 13.80 13.46 0.35

Group B Male 20 14.10 – 15.60 14.92 0.51

(14 – 16 years) Female 20 14.20 – 15.70 15.08 0.49

Group C Male 20 16.10 -‐17.90 17.09 0.59

(16 – 18 years) Female 19 16.10 – 17.60 16.87 0.49

5.5.2 Measurement normality

Normality was tested using the Shapiro-‐Wilk method. In the male sample 39 out of

40 measurements were normally distributed. The base width of proximal phalanx

five was the only measurement that was not normally distributed. A total of five

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66

measurements in the female sample were found to violate assumptions of

normality; the base width of metacarpal two, proximal phalanx two and proximal

phalanx three, and the head width of proximal phalanx two and four. These

measurements were not removed as further analyses of these measurements were

considered to be relatively robust to violations of normality (as discussed above).

5.5.3 Univariate comparisons

The descriptive statistics for the sub-‐adult hand bone measurements are presented

in Appendix Four. In Group ‘A’, the males have (in general) smaller mean

measurement values than the females of the same age group. It is evident that from

approximately 14 years of age, however, males have larger mean measurement

values than females. The results from the ANOVA comparisons show a tendency

for F-‐statistic values to increase as the age of the sample group increases

(Appendix Four). The majority of mean differences between males and females in

Group ‘A’ were found to be insignificant and more measurements start to become

statistically significantly different between males and females in Group ‘B’. All

comparisons in Group ‘C’ were found to be statistically significantly different.

5.6 Sex classification accuracy in the sub-‐adult hand

i) Discriminant function analysis

Stepwise discriminant function analysis was conducted for each of the three sub-‐

adult age groups (Table 5.11). The function produced for group ‘B’ had the highest

cross-‐validated accuracy of 95.00% using five measurements; however, the sex

bias was 10.00%. Group ‘A’ had the lowest cross-‐validated accuracy of 76.20% and

an associated sex bias of 8.30%. Group ‘C’ had a cross-‐validated classification

accuracy of 92.30%, but also had the largest sex bias value of the three groups

(-‐15.00%).

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67

Table 5.11 Stepwise discriminant functions based on the analysis of the sub-‐adult sample

Groups Step #Variables Unstandardised coefficient


Wilk’s lambda

Structure point

Group centroids

Sectioning point

Correctly assigned

Sex bias

Function 19.

(Group A)

1

WMPP4 Constant

1.332 -‐10.738

1.000 0.655

1.000

♂ 0.723 ♀ -‐0.658

0.0325 76.20% 8.30%

Function 20.

(Group B)

1 2 3 4 5

WBMC1 WMMC4 WHPP4 WBMC4 WMPP3 Constant

0.587 1.210 -‐1.221 0.720 0.922 -‐20.614

0.516 0.791 -‐1.213 0.671 0.762

0.482 0.381 0.325 0.263 0.216

0.545 0.471 0.200 0.322 0.490

♂ 1.857 ♀-‐1.857

0.00

95.00%

-‐10.00%

Function 21.

(Group C)

1 2 3

WHPP2 WBMC2 WMMC1 Constant

0.917 0.405 0.583 -‐23.560

0.599 0.490 0.453

0.318 0.285 0.280

0.724 0.687 0.507

♂ 1.706 ♀-‐1.795

-‐0.0445

92.30%

-‐15.00%


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68

ii) Classifying sub-‐adults using an adult classification model

To explore the effect of applying an adult discriminant function to sub-‐adults, the

adult stepwise discriminant function (Table 5.7, Function 13) was used to classify

the sub-‐adults in each age group. For all three groups, the female individuals were

all correctly classified and for the males the highest classification accuracy

achieved was 65.00% for Group ‘C’. Group ‘C’ also had the smallest sex bias (-‐

35.00%), whilst group ‘A’ had the largest sex bias (-‐70.00%). Clearly, however, all

of the sex bias values render this function not forensically applicable to the sub-‐

adult population.

Table 5.12 Sex classification accuracies of adult Function 13 to the sub-‐adult sample

Group Number of males correctly classified

Number of females correctly classified

Sex bias

A 3/10 (30.00%) 10/10 (100.00%) -‐70.00% B 9/20 (45.00%) 20/20 (100.00%) -‐55.00% C 13/20 (65.00%) 19/19 (100.00%) -‐35.00%

5.7 Interaction effects

Regression analysis was performed to test for interaction effects between sex and

age to evaluate whether age has any effect on strength of the relationship between

hand size and sex. No statistically significant interactions were found between age

and sex for group ‘A’, ‘B’ or ‘C’. This suggests that any statistically significant

differences found between sub-‐adult males and females are independent of age.

69

CHAPTER SIX

Discussion and conclusions

6.1 Introduction

The purpose of the present study was to quantify the magnitude of hand bone

sexual dimorphism and to concurrently establish sex estimation standards for a

Western Australian population. The latter is important because sex estimation is

more accurate when the standards applied are population specific. A precision test

was conducted prior to data collection to establish the most reliable method for

acquiring linear data from hand x-‐rays. Thereafter, the ‘line-‐tool’ method was used

to acquire adult and sub-‐adult measurements that were statistically analysed to

quantify the level of morphometric dimorphism. Discriminant function analysis

was then performed to quantify the accuracy of sex estimation; the results of those

analyses are discussed, in addition to considering forensic applications, limitations

and future research directions. A final conclusion is then presented.

6.2 Measurement precision

The first aim of the present study was to assess which method of acquiring

measurements in digital x-‐rays was the most accurate, reliable and practical for

data acquisition. A precision test (technical error of measurement, relative

technical error of measurement and coefficient of reliability) was performed using

the landmark and line-‐tool acquisition methods. This was important because

unacceptably high measurement error can affect the validity of the study;

specifically the robustness of the subsequent statistical analyses and prediction

models.

All hand bone measurements acquired using both methods produced ‘acceptable’

rates of measurement error (See Table 5.1 and 5.2). The rTEM is a standardised

version of TEM and measurements are considered imprecise if this statistic is

above 5% (Goto & Mascie-‐Taylor 2007; Ulijaszek & Kerr 1999). The R value

provides an estimation of how much variation is not attributable to measurement

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70

error and thus R values closer to 1 are indicative of precise measurement

acquisition. Overall, the line-‐tool method had a lower average rTEM (1.33%) than

the landmark method (1.77%) and more measurements with an R value equal to 1

(20 compared to 12 for the landmark method). The slight difference in these

statistics between the line-‐tool and landmark methods are likely related to

differences in the measurement system affecting repeatability. For example, the

landmark method required the location of extremal landmarks in each hand bone,

which are known as “Type III landmarks” (Bookstein 1991, pp.65; O’Higgins 2000).

The 3D (x, y, z) co-‐ordinates of the landmarks were then used to calculated the

inter-‐landmark distances, without knowing whether the placement of those

landmarks would result in a true maximum linear distance. The line-‐tool method,

however, involves drawing a line between two landmarks, which enables a direct

measurement. Once the line was drawn, each end of the line could be moved

medio-‐laterally or in a proximal/distal direction in order to find a true maximum

linear distance. The latter resulted in greater consistency between the repeated

measurements, as verified by the precision test results (Table 5.2).

The rTEM and R values in the present study are consistent with previous research;

R values > 0.9 and rTEM values < 5% for linear measurement acquisition (e.g.

Arroyo et al. 2010; Franklin et al. 2012b; Ulijaszek and Kerr 1999; Ishak et al.

2012). The precision test confirms that accurate measurements can be taken in

digital hand x-‐rays, as previously suggested by Verhoff et al. (2008) and Franklin et

al. (2012c), who both examined measurement precision in actual bone specimens

and multi-‐slice computer tomography scans. Verhoff et al. (2008) assessed the

variance between measurements acquired from actual skull specimens and their

three dimensional (MSCT) reconstructions. The physical and digital measurements

varied on average by 1-‐3mm. Franklin et al. (2012c) reported an even smaller

difference when comparing traditional cranial measurements acquired in physical

bone specimens and their MSCT counterparts (digitised in OsiriX®); the mean

difference was only 0.9mm and statistically non-‐significant between the two

acquisition methods.

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71

6.3 Adult data

6.3.1 Sexual dimorphism in the hand

As the overall purpose of this study was to produce population-‐specific sex

estimation standards, the magnitude and expression of sexual dimorphism in the

hand bones was first quantified. The ability to accurately estimate sex in the

human skeleton is inherently related to sex-‐specific morphological and

behavioural differences (Glücksmann 1981; Frayer & Wolpoff 1985). The degree to

which sexual dimorphism is expressed, and the age at which males and females

begin to vary morphologically, is dependent on genetic and environmental factors

that differ between populations. For this reason, therefore, it is recommended that

sex estimation standards are population specific, which affords the most accurate

possible sex classification (Burrows et al. 2003; Franklin et al. 2012b; Franklin et

al. 2013).

i) Hand bone measurements

In the present study mean male measurements were significantly larger than their

female counterparts (See Table 5.4). In general, the base width, head width and

mid-‐shaft width measurements were more dimorphic (greater ‘between-‐sex’

variance) compared to the maximum length measurements. The latter is congruent

with previous research in British (Scheuer and Elkington 1993), Spanish (Barrio et

al. 2006) and Greek (Manolis et al. 2009) populations. Scheuer and Elkington

(1993) reported their highest index of separation (the difference between the male

and female mean divided by the pooled standard deviation) for the mediolateral

base width of metacarpal two, whilst Barrio et al. (2006) reported that this same

measurement resulted in the highest sex classification accuracy (91.00%). Manolis

et al. (2009) also suggest that epiphyseal widths are the most accurate indicators

of sex, as they generally exhibit morphometric sexual dimorphism to a greater

extent than length measurements. Previous studies examining the fleshed hand

also suggest that width and breadth measurements are more sexually dimorphic

than length measurements. (e.g. Ishak et al. 2012, Alboul-‐Hagag et al. 2011).

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72

The observation that hand width measurements (both skeletal and fleshed)

express a greater degree of sexual dimorphism could be related to functional

loading in response to mechanical stimulus (e.g., Moss 1980; Trinkaus et al. 1994;

DiBennardo & Taylor 1979; Carter et al. 1996). Functional loading (put simply) is

the transformation in size, shape or mass reflected in the distribution of cortical

bone that skeletal tissue can potentially undergo in response to mechanical loading

or “…patterns of habitual biomechanical stress” (Trinkaus et al. 1994, pp.2; see also

Moss 1980).

Trinkaus et al. (1994) and Wilczak (1998) suggest that longitudinal measurements

(such as maximum length) are less likely to be affected by any physical stresses

resulting from habitual activity. Trinkaus et al. (1994), in particular, compared the

humeral diaphyseal and articular dimensions in six different groups; a

Neanderthal sample and five recent human groups from different populations (one

of which consisted entirely of professional tennis players). Trinkaus et al. (1994)

concluded that variance in diaphyseal measurements were heavily influenced by

mechanical loading, as evidenced by the high level of bilateral asymmetry

exhibited by the professional tennis players (maximum of 57% difference in

diaphyseal dimensions), as compared to the four ‘recent’ human samples

(maximum of 14% difference). Maximum bone length and articular dimensions

were found to exhibit minimal levels of asymmetry across all six samples.

Wilczak (1998) reported similar results when comparing mean muscle insertion

areas, as well as length and articular dimensions, of African-‐American, Euro-‐

American and four American Indian populations. Not only did diaphyseal (muscle

insertion) areas differ between individuals of a different ancestral background

(African-‐American, Euro-‐American and American Indian), they also differed within

the four Native American groups. They suggest that this was likely due to different

subsistence methods, and concluded that diaphyseal breadths are more sensitive

to mechanical stresses (after skeletal maturation) than are length and articular

dimensions (Wilczak 1998).

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73

ii) Sex estimation potential

Sex is often the first component of the biological profile estimated because the

remaining elements (age, stature and ancestry) are generally estimated using sex-‐

specific methods (Franklin 2012a, Braz 2009). It is important, therefore, to use the

most accurate sex estimation standards, because misclassification may have a flow

on effect that reduces the accuracy of the estimation of other biological attributes.

A series of single variable discriminant analyses were performed to derive a

classification function for each metacarpal and phalanx analysed (See Table 5.5);

combined cross-‐validated accuracies ranged from 76.70% (mid-‐shaft width of

metacarpal four) to 87.00% (base width of proximal phalanx three). The

classification potential of using all four measurements from a single metacarpal

was also considered; however, only two of the resulting functions had cross-‐

validated accuracies over 80% (See Table 5.6). A series of stepwise discriminant

function analyses based on measurements from the complete hand, and from each

individual digit, were also performed (See Table 5.7). The stepwise analysis of all

hand bone measurements resulted in the highest cross-‐validated accuracy

(91.00%; sex bias -‐6.00%) based on the analysis of eight variables.

As discussed previously (see above) the width measurements were more sexually

dimorphic than the maximum length measurements. This was further confirmed

by examining the functions that had accuracy rates above 85%; there were more

width measurements included in these functions, which also had higher

standardised coefficient values than the length measurements (if length

measurements were included at all). However, functions with classification

accuracies over 85% are not necessarily reliable if they misclassify either males or

females at disproportionate levels. For this reason, the sex bias was calculated and

functions with a bias greater than ± 5% (such as Function 8; classification accuracy

of 87.00% and sex bias of -‐7.40%) should thus be applied with caution, despite a

high degree of expected cross-‐validated accuracy.

In the present study, the lowest cross-‐validated classification accuracy (76.70%)

was for Function 4 based on the measurement of the mid-‐shaft width of

metacarpal four (See Table 5.5). The highest cross-‐validated classification accuracy

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74

(91.00%) was for Function 13, requiring a total of eight hand measurements in the

hand (See Table 5.7). Generally, the functions that included multiple

measurements achieved a higher accuracy; this observation is also consistent with

previous research (e.g. Scheuer & Elkington 1993; Barrio et al. 2006; Case & Ross

2007).

There are a number of studies that have examined the sex estimation potential of

hand bones within different ethnic groups, including: Caucasian British (Scheuer

and Elkington 1993); Caucasian and African American (Burrows et al. 2003; Case

and Ross 2007); Spanish (Barrio et al. 2006); and Egyptian (El Morsi and Hawary

2012) populations. The results of the latter studies are summarised in Table 6.1.

The range of sex classification accuracy in the present study (76.70 -‐ 91.00%) is

well within that of the published literature. When comparing the results of the

present study to the global populations in Table 6.1, the classification accuracy

range for the Egyptian population (66.80 -‐ 83.90%) is clearly the most different.

However, the results of the latter study are obviously different compared to all of

the global populations listed. This is likely due to the lack of stepwise discriminant

functions in the Egyptian study or the exclusion of width measurements. As

already discussed, width measurements were found to be the most dimorphic

variables ; and as they were not considered in the Egyptian study, this may be why

classification accuracies were not as high as that of the current study. From Table

6.1 it is evident that the classification accuracy ranges achieved for the current

study were most similar to those of Scheuer and Elkington (1993). This similarity

could be due to the common ethnic origin of both populations, which is supported

by 2011 census statistics for Western Australia, where 79% of that population are

self-‐reported to be of English ancestry (ABS 2012).

The current study achieved the third highest classification accuracy (91.00%) of

the five studies listed in Table 6.1. However, the two studies that reported higher

classification accuracies were based on considerably smaller samples (n<72), as

opposed to the present study (n = 300). In considering a larger sample size, the

results from the current study are more statistically robust, as they are based on a

broader cross-‐section of the local population. The discriminant functions produced

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75

are, therefore, more likely to perform with comparable accuracy when applied to

the broader Western Australian population.

Table 6.1 Sex classification accuracy of adult hand bone measurements in a variety of global populations.

Publication Population Sample size (n) Classification accuracy range

Current study Western Australian ♂ =150 ♀ = 150 76.70 -‐ 91.00%


Caucasian-‐British ♂ =33 ♀ = 27 74.00 -‐ 94.00%

Barrio et al. 2006

Spanish ♂ =36 ♀ = 36 81.20 -‐ 91.40%

Case and Ross 2007 Caucasian-‐American and European

♂ =133 ♀ = 116 77.90 -‐ 84.30%


Egyptian ♂ = 100 ♀ = 100 66.80 – 83.90%

In the present study posterior probabilities were calculated for all 18 functions

(see Appendix One); these are used as a statistical indication of confidence in

classifications. Higher posterior probability values are indicative of a discriminant

score that is well above (or below) the sectioning point (Patriquin et al. 2005). The

function that produced the highest classification accuracy (Function 13) had the

highest percentage of individuals classified at 80% certainty and above (90.59%),

thus suggesting it is unlikely those individuals were classified by chance. No

individuals within the sample were classified at lower than 40% certainty. This

provides some degree of statistical confidence that functions derived from the

Western Australian sample should be applicable to the broader population in a

forensic context. Functions 1 to 6 had the lowest percentages of individuals

(ranging from 55.80 to 64.82%) classified at 80% certainty or above. This would

suggest that the functions based on a sole metacarpal variable are not as robust as

the direct multiple variable and stepwise discriminant functions.

In considering the accuracy of skeletal sex assessments in general, the pelvis and

cranium are the preferred bones used to estimate that particular biological

attribute (Franklin et al. 2014). The pelvis, in particular, is considered the most

dimorphic bone as it has a specific morphology related to biological function (e.g.

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76

accommodating childbirth in females). For the pelvis alone, sex classification

accuracy rates have been reported between 90 to 95% (Phenice 1969; Lovell

1989; Ubelaker & Volk 2002). High classification accuracies have also been

reported for the skull, ranging from 75 to 90% (e.g. Giles & Elliot 1963; Franklin et

al. 2005) and the long bones: between 83 to 96% for the humerus (Spradley &

Jantz 2011; Robinson & Bidmos 2008; Albanese et al. 2005); 85 to 94% for the

radius (Spradley & Jantz 2011); 76 to 97% for the femur (Albanese et al. 2008;

Robinson & Bidmos 2011; Spradley & Jantz 2011); and 54 to 91% for the tibia

(Robinson & Bidmos 2011; Spradley & Jantz 2011). The range of classification

accuracies resulting from the standards presented in the present study are

comparable to those previously reported for other skeletal elements; further

confirming that hand bones from a Western Australian population are sexually

dimorphic and can be used to accurately estimate sex.

6.3.2 Morphometric population variation

A series of independent sample t-‐tests were conducted to investigate the

significance of population variation in hand bone measurements. It should be

noted that not all of the comparative studies acquired data from radiographs.

Comparisons are therefore made with caution. The maximum length

measurements of metacarpals one, two and four were compared, because they

were measurements common to the present study and the four comparative

studies. When comparing Western Australian male mean metacarpal length values

to the four comparative populations, all but one comparison was statistically

significantly larger (maximum length of metacarpal one for the Western Australian

and Egyptian individuals.). A similar trend was also examined when comparing

Western Australian females to the comparative populations; again, the only

statistically insignificant difference was found for metacarpal one to the Egyptian

population.

The relative magnitude of sexual dimorphism expressed in the hand bones was

also compared between populations; Western Australian population was most

similar to the British population. The purpose of these t-‐tests was to observe if

there was a difference in both mean metacarpal values and the magnitude of

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77

sexual dimorphism expressed between populations. Due to the degree of variation

between these populations (both morphometrically and in the relative magnitude

of sexual dimorphism expressed by the hand bones), life-‐time activity, prevalence

of malnutrition, climate and temporal (or secular) variation are all considered as

potential contributors to the variation assessed and are further discussed below.

i) Life-‐time activity

Life-‐time activity, both during and after skeletal maturation, can affect bone size

and shape (Wells 2007). Based on the analysis of the effect of improved nutrition

over time on the length and proportion of long bones, Jantz and Jantz (1999)

suggest that longitudinal bone dimensions (such as length) are less likely to be

affected by “patterns of habitual biomechanical stress” once skeletal maturation has

been reached. Both male and female long bone lengths were found to increase by

less than 1% per decade. Studies that have examined population variation in

skeletal robusticity (e.g. Collier 1989; Wilczak 1998) independently confirm this

trend, as both studies demonstrated population variation in diaphyseal and

epiphyseal, rather than length, measurements. For this reason, life-‐time activity

was dismissed as a factor affecting the morphometric variation in length

measurements between the populations examined in the present study.

ii) Malnutrition and quality of life

Malnutrition is a health issue concerned with the disproportionate consumption of

nutrients; either too much, not enough, in the wrong proportions, or affected by

malabsorption (World Health Organisation 2014). Malnutrition is measured by the

World Health Organisation (2013a) as the occurrence of height stunting;

calculated as the percentage of children under five years old who were more than

two standard deviations shorter than the expected height for their age. World

Health Organisation statistics (2013a), however, suggest that malnutrition in

children under five years of age in Australian and American populations is

minimal; metacarpal length differences between these populations is therefore,

most likely a result of factors other than inadequate nutrition. Data acquired from

an Australian sample in 2007 indicated that only 1.8% of children under the age of

five fell into this category. For America, the same statistic (as of 2002) is 3.9%

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78

(World Health Organisation 2013a). No such statistics were found for Spain and

the United Kingdom. However, as Western Europe (as for Australia and America)

is considered a developed region, it is unlikely that Spain and the United Kingdom

would have a significantly high prevalence of malnutrition. It is therefore also

unlikely that malnutrition is a causative factor for the existing morphometric

variation between the current study and the aforementioned comparative studies.

Egypt had the highest percentage of children with a height less than two standard

deviations expected for their age with the World Health Organisation (2013a;

2013c) reporting 30.7% of children under five exhibiting height stunting as of

2010. It is possible that malnutrition has been an influencing factor on metacarpal

length.

Along with malnutrition, the difference in ‘how developed’ the five populations are

was also considered. Comparative measures (such as the quality of life index and

the human development index) and statistics such as mortality rate, and life

expectancy at birth, are presented in Table 6.2.

Table 6.2 Quality of life statistics, quality of life index and human development

index for each of the five comparative populations.

Australia Egypt Spain United Kingdom

United States

Mortality rate# i, ii, iii, iv, v. 5 21 4 5 8

Life expectancy at birthvi 82 73 82 80 79

Quality of Life Indexvii Score 7.925 5.605 7.727 6.917 7.615 Rank 6 80 10 29 13

Human Development Indexviii

Score 0.938 0.662 0.885 0.875 0.937 Rank 2 112 23 26 3

Key: #. Mortality rate of children under five per 1000 live births; i. WHO 2013b; ii. WHO 2013c; iii.

WHO 2013d; iv. WHO 2013e; v. WHO 2013f; vi. OECD 2006; vii. The Economist 2005; viii. UNDP

2011.

The quality of life index is a score (out of 10) that is calculated using information

including (but not limited to) overall population well-‐being, health, family

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79

relations, job security, and political freedom (The Economist 2005). Out of 111

countries, Australia is ranked the highest of the five populations considered at

number six with a score of 7.925 (Table 6.2). Spain (number 10), America (number

13) and the United Kingdom (number 29) all rank in the top 30% of countries with

scores of 7.727, 7.615 and 6.917 respectively. Egypt, however, is within the bottom

30% (number 80) with a score of 5.605.

The human development index is another comparative measure based on factors

such as life expectancy, literacy skills, education, standards of living, and overall

quality of life. This index is on a scale from zero to one; values closer to zero are

indicative of an underdeveloped country and vice versa. Australia has the highest

human development index (0.938) and is placed at number two out of 186

countries (UNDP 2011). Egypt is, once again, the lowest ranked out of the five

countries considered (number 112), with the lowest human development index of

0.662, suggesting it is the least developed of the five populations. Quality of life is

also expressed through life expectancy and child mortality statistics. Egyptians

have a lower quality of life than Australian, Spanish, British and American

individuals. For instance, not only do Egyptians have a lower life expectancy at

birth (73 years old) than the four ‘more developed’ populations, the mortality rate

for children under five years is much higher (21 per 1000 live births) than any of

the other populations considered.

The statistics and indices outlined in Table 6.2 suggest that the quality of life in

Australia is similar to the quality of life in Spain, the United Kingdom and the

United States. It is, therefore, unlikely (as with the prevalence of malnutrition) that

difference in quality of life is a contributing factor to the morphometric differences

and differences in the expression of sexual dimorphism that exist between the

Western Australian population and the Spanish, British and American populations.

Whether prevalence of malnutrition and difference in quality of life are factors

relating to the unexpected similarities between the Western Australian and

Egyptian populations is uncertain. However, when considering the prevalence of

malnutrition and quality of life, it was expected that the Egyptian population

would exhibit the smallest magnitude of sexual dimorphism. Individuals of an

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80

Egyptian population are more likely to be subjected to poor nutrition, inadequate

access to water and lower quality of life than individuals from the more

‘developed’ comparative populations. It is therefore possible that individuals from

the Egyptian population study would not reach their full “genetic potential for

growth” (Mielke et al. 2011, pp.274) as a result of poor nutrition and quality of life.

Populations from countries that are considered to be less (or under) developed

have a tendency to express sexual dimorphism at a smaller magnitude than

populations from developed countries. This a reflection of males being more

susceptible to growth stunting factors (such as poor nutrition) than females

(Harrison et al. 1977) resulting in males and females being morphometrically

similar and less sexually dimorphic. This, however, was not what the results of the

t-‐test depicted.

iii) Climate

Another factor that can affect levels of morphometric variation, or the degree of

sexual dimorphism expressed by the hand bones in different populations, is

climate. With regards to biological and morphological variation, Allen (1877) and

Bergmann (1847; cited in Collier 1989) proposed rules concerning body

composition in relation to climate. Bergmann’s rule suggests that individuals with

a larger body-‐weight in proportion to their overall body size are more suited to

cooler climates, and individuals with a smaller body weight in proportion to their

overall body size, are more suited to a warmer climate. This negative correlation

between body-‐weight and mean temperature was confirmed by Roberts (1953);

the mean body-‐weight of populations from Africa, South-‐East Asia and Australia

were significantly lower than the mean body-‐weight of American and European

populations. As the present interpretation is focussing on population variation in

metacarpal length, Allen’s rule is more relevant to this comparison than

Bergmann’s rule.

Allen’s rule concerns the length of extremities in relation to climate; individuals

subject to cooler climates are expected to have shorter limbs, along with a lower

surface area to allow for sufficient heat retention (Allen 1877). In warmer climates,

the opposite is expected; longer extremities coupled with a larger surface area

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81

(relative to body size) to decrease heat retention (Allen 1877; Collier 1989). It is,

therefore, expected that taller individuals would have longer limb bones and by

association (resulting from a scaling effect) longer hand bones. Both Western

Australia and Spain are considered to have Mediterranean climates, and it was

therefore expected that the mean metacarpal length values may be similar (Bureau

of Meteorology 2014). As the Egyptian population are subject to a more arid

climate, reaching higher maximum temperatures than Western Australia, Egyptian

mean metacarpal length values were expected to be larger than their Western

Australian counterparts (Egyptian Meteorological Authority 2014; AEMET 2014).

The results of the present study, however, do not reflect the latter relationship.

The results (Appendix Two) suggest that Western Australian metacarpal lengths

for both males and females are significantly larger than those of Spanish

individuals. The metacarpal lengths of Western Australian individuals were

actually found to be most similar to the Egyptian individuals, despite the

expectation that the Western Australian population would be significantly smaller.

As with the previous factors discussed, climate alone cannot explain the

differences and similarities (and dissimilarities) found when comparing the

Western Australian and comparative populations. Whether climate actually has, or

the extent climate does have, an effect on hand bone size is uncertain. In a

historical context, this relationship between climate and body shape and size

would have been more likely. However, in a modern context, humans can

effectively adapt their environments to suit them; thus minimising the effect

climate has on body type and size (i.e. Allen’s and Bergmann’s rules).

iv) Secular variation

In examining the comparative populations it is evident that the Caucasian-‐British

(Scheuer & Elkington 1993), Spanish (Barrio et al. 2006) and Caucasian-‐American

(Case & Ross 2007) are temporally removed to the contemporary Western

Australian sample (Table 6.3). This temporal difference may be the reason for the

size difference examined; secular changes in height as well as long bone size and

proportion are all well documented (e.g. Tanner 1962; Van Wieringen 1986; Jantz

& Jantz 1999; Bielecki et al. 2012). Secular changes in height have been established

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82

as a positive linear trend with an average 1-‐1.5cm increase in mean height every

decade, regardless of population (Tanner 1962; Van Wieringen 1986). In Polish

school-‐aged boys an increase of up to 3.43cm in mean height was found when

compared data from the previous decade (Bielecki et al. 2012). Ljung et al. (1974)

also found a secular change in mean height in Swedish children (measured

between 1965 and 1971) who had a larger mean height (an average increase of

2.5cm every decade) than Swedish children measured in 1938. Similar results

were found when comparing data on stature and weight acquired from Melbourne

school students (5 to 17 years of age) with data acquired from up to 100 years

prior; from 1970 to 1992, a 1.2 centimetre increase per decade was for found for

male height at age 17 and a 0.2 centimetre increase per decade was found for

females height at age 17 in the Victorian (Australian) population (Loesch et al.

2000).

Table 6.3 Year of birth and year of death ranges of the three temporally different comparative studies.

Publication Sample Year of birth

range Year of

death range

Current Study Digital hand x-‐rays from various hospitals in Western Australia

1948 – 1995 NA


Cadavers from various medical schools in United Kingdom

1844 – 1930 1926 -‐ 1988

Barrio et al. 2006 Skeletal collection from Complutense University of Madrid

Unknown 1975 – 1985

Case and Ross 2007 Terry anatomical collection 1840 – 1950 1920 -‐ 1965

Key: NA = Not Applicable

With gradual secular generational increases in mean height values, it is likely that

changes have concurrently occurred in mean metacarpal length values. For

example, Jantz and Jantz (1999) found that the secular increase in long bone length

for both lower and upper limb bones had a similar gradual increase as height when

comparing generations. This may explain the more significant differences, between

mean metacarpal lengths from the current study and Scheuer and Elkington

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83

(1993), Barrio et al. (2006) and Case and Ross (2007) (See Appendix Two). Due to

a temporal variation between the latter three studies and the contemporary data

examined in the present study, differences in mean metacarpal lengths could be

the result of secular variation. With improved nutrition, sanitation, accessible

medical facilities and reduced incidence of childhood or infectious diseases,

contemporary populations have a tendency to exhibit larger bone dimensions than

previous generations by reaching their full “genetic growth potential”(Mielke et al.

2011, pp.274; Eveleth & Tanner 1976; Malina 1979).

v) Summary

The results from the independent sample t-‐tests confirm morphometric variation

in the hand bones. Furthermore, t-‐test results also confirm population differences

in the magnitude of sexual dimorphism. Differences in life-‐time activity, the

prevalence of malnutrition, climatic and temporal variation were all considered.

However, morphometric differences and variation in the magnitude of sexual

dimorphism is likely the result of multiple and largely unpredictable factors.

Without knowing which factors have had an effect on each of the populations, and

how much these factors have contributed to the expression of sexual dimorphism,

sex estimation standards based on morphometric hand bone data cannot be

accurately applied to foreign populations. This further emphasises the need for

population specific standards (see below).

6.3.3 Importance of population specific standards

Non-‐population specific standards may result in inaccurate results because

populations are known to differ in the expression and magnitude of sexual

dimorphism (Frayer & Wolpoff 1985). This issue has been investigated previously;

for example, Burrows et al. (2003) tested the applicability of previously

established sex estimation standards based on populations foreign to their

American sample. Sex-‐estimation standards for Caucasian-‐British (Scheuer and

Elkington 1993), mixed Caucasian-‐European and Caucasian-‐American (Case and

Ross 2007) and Caucasian-‐American and African-‐American were evaluated

(Stojanowski 1999). Sex estimation accuracy using the foreign models were up to

9.00% lower and the range of achieved accuracies was wider (e.g. Burrows

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84

reported an accuracy range of 65.2 to 95.7% when the original reported accuracy

range was 75 to 90%), indicating less consistency than originally reported.

However, the foreign estimation standards were tested on a relative small sample

(n=23), which had an age at death range of 64-‐93 years.

In the present study there was a tendency (similar to that demonstrated by

Burrows et al. 2003) for classification accuracies to be lower than initially reported

when foreign standards were applied to the Western Australian population. The

most accurate foreign standards (when applied to Western Australia) were those

of a North-‐American Caucasian population (Case and Ross 2007) however, due to

its extremely high sex-‐bias (48.67%), it would be completely unacceptable to use

this standard to estimate sex in Western Australian individuals.

6.4 Sub-‐adult sample

i) Sexual dimorphism

Another aim of the present study was to assess whether sex can be accurately

estimated in sub-‐adults and to determine at what age the hand bones are

quantifiably dimorphic. The results from the univariate comparisons suggest that

the hand bones are sexually dimorphic at approximately 14 to 16 years of age

(Group B; 14-‐16 years). The number of statistically significant sex differences

increased with increasing age. This likely related to the age of pubertal onset in

both males and females and the difference that exists in both pubertal and skeletal

growth between males and females (Gasser et al. 2013).

Franklin et al. (2007) examined the sex estimation potential of the mandible in

sub-‐adults from three separate populations, with results also suggesting that

males and females become morphologically dimorphic around the age of 15 years.

Although in that study multivariate regression analyses indicated that there were

no statistically significant differences between sub-‐adult males and females, it was

suggested that sex estimation could be possible at the age of 15 years, as p-‐values

for this age group were approaching significance. Franklin et al. (2007) proposed

that statistically significant differences may have been found had a larger sample

been considered.

CHAPTER SIX

85

Gasser et al. (2013) documented the appearance of several stages of pubertal

growth and the pubertal growth spurt in 120 boys and 112 girls from a Swiss

population. Females within this study were found to begin their pubertal growth

spurt approximately one year earlier than males, with their peak growth velocity

occurring between 12 to 12.5 years of age. The onset of the pubertal growth spurt

for the males in that study was between 10.75 and 11 years, with their peak

growth velocity occurring between 13.5 and 14 years of age. In considering the

results of the present study, the latter may explain the few statistically significant

differences between the male and female mean values in Group ‘A’ (12 to 14 years)

as opposed to Groups ‘B’ (14 to 16 years) and ‘C’ (16 to 18 years). Data acquired

from this same Swiss sub-‐adult sample was assessed for the onset, peak growth

velocity and maximal deceleration of skeletal growth, resulting in similar ages to

the stages of pubertal growth (Molinari et al. 2013). Peak skeletal growth occurred

at approximately 12.2 years in females and 14 years in males, which once again is

reflected in the increase of statistically significant differences between the sexes as

age increases.

ii) Sex classification accuracy

Stepwise discriminant functions were formulated for the three sub-‐adult age

groups. Group ‘B’ and ‘C’ had classification rates above 90%, which would suggest

that from 14 years of age sex can be accurately estimated. Despite achieving cross-‐

validated accuracies over 80% for Groups ‘B’ and ‘C’, their associated sex bias

values were greater than ± 5%, suggesting that these functions should be used

with due caution. When considering the sex bias values (-‐35 to -‐70%) sex

estimation of sub-‐adults is deemed to be unreliable and inaccurate. This may be

due to variation in the onset of pubertal growth and skeletal maturation, and the

ages associated with these two stages of development (see above). It is possible

that sex bias values would decrease if a larger sample for each age group was

considered, as a larger sample would include a broader cross-‐section of the sub-‐

adult population and therefore more variation.

Additionally, the adult sexing standard (Function 13) was also applied to the sub-‐

adult sample to establish whether the former is applicable to the non-‐skeletally

CHAPTER SIX

86

mature hand. It was clear the function based on the adult data is not suitable for

the application to sub-‐adults. For all three groups, the total number of females was

correctly classified (100% correctly classified). However, across the three groups,

the highest number of males correctly classified using the adult discriminant

function was 65.00%. This tendency for males to be misclassified as females may

be due to their later pubertal growth spurt and therefore, their later completion of

skeletal growth (Molinari et al. 2013). At the final stages of the pubertal growth

spurt, females were found to have reached 97% of their total adult height at the

age of 13.5 years, and males were found to have reached 96% of their total adult

height at 15.5 years. As the age limit of the individuals included in the sub-‐adult

sample was 17.90 years, it is possible that some of the male sub-‐adults classified

using the adult function were still growing and, therefore, were classified as female

due to their smaller hand dimensions.

In light of this possibility, Group C was scrutinised for skeletal maturity; all 20

females were observed to be skeletally mature (complete epiphyseal fusion) and

18 of the 20 males were skeletally mature. However, both male individuals with

incomplete fusion were correctly classified by the adult Function 13, negating the

suggestion that skeletal maturity is responsible for the sex bias observed. It is

beyond the scope of this research study to evaluate the reasons for the high degree

of male misclassification in skeletally mature sub-‐adults, although it is inevitably

related to small sample size, in collaboration with factors such as ancestry.

6.5 Forensic applications

The classification models produced in this study are designed for forensic

application when assessing sex from digital hand radiographs in Western

Australia. The functions presented are suited to varying levels of bone

preservation. In an ideal situation, data would be acquired from the complete

hand in order to classify sex using the stepwise model (Function 13) as it had the

highest classification accuracy and a reasonable sex bias value. However, the

likelihood of recovering a complete skeletal hand when a human body has already

progressed to the skeletal stage of decomposition is relatively lower compared to

other larger and more robust skeletal elements. Waldron (1987) calculated

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87

percentage recovery rates for different archaeological skeletal elements and found

a relatively strong correlation between the survival of skeletal elements and their

size. The phalanges were among the least well-‐represented skeletal elements,

likely due to their smaller size. Metacarpals were considered to be ‘fairly’ resistant

to post-‐mortem damage with recovery rates ranging from 39.80% for the right

fifth metacarpal to 63.60% for the left third metacarpal. Forensic cases are likely to

exhibit percentage recovery rates similar to those found by Waldron (1987) or

perhaps even worse depending on the manner of death (e.g. post-‐blast remains).

For this reason, sex estimation standards were formulated based on a single hand

bone; as these models do not require a complete hand, they can be applied in

situations where the hand bones have been disarticulated, fractured or in cases of

co-‐mingled amputated limbs, such as what may occur in disaster victim

identification (DVI) situations (Blau & Briggs 2011).

6.6 Limitations and future research

The current study examined both adult and sub-‐adult Western Australian

individuals. The large adult sample (n=300) afforded a robust analysis of the data

acquired, as potential sampling error would have been minimised. The number of

sub-‐adults from each of the three age groups examined, however, was

comparatively small (approximately 20-‐40). Further research into the sex

estimation potential of the sub-‐adult hand, based on the analysis of a larger

sample, would be beneficial. Research into bilateral asymmetry could also be

performed to assess the applicability of classification models from this study

(based on right hand measurements) to the left hand. Ishak et al. (2012) confirmed

that bilateral asymmetry had minimal effect on measurements from the fleshed

hand in a Western Australian population. This is also likely to occur for the hand

bones, but it is yet to be statistically quantified.

Another consideration for future research would be to conduct a longitudinal

study on morphometric differences in long bones, specifically including the

metacarpals and phalanges, to assess the magnitude of secular differences in

Western Australian individuals across generations. This may give an indication of

how reliable the classification models produced would be to past and future

CHAPTER SIX

88

generations, and perhaps necessitate the formulation of updated contemporary

classification models in the future.

Finally, it would be of interest to acquire data from an adult sample of known

ancestry. From the 2011 census, the Australian Bureau of Statistics reported that

over 25% of the Western Australian population were born overseas, whilst 20% of

the remaining population had at least one parent born overseas (Australian

Bureau of Statistics 2013). It would be to ascertain if accuracy varies between the

different ethnic groups in the Western Australian population (e.g. Asian and

European ancestry). Furthermore, the standards produced from this study should

be used to classify individuals from different states within Australia to establish if

these standards can be applied to the Australian population as a whole, as opposed

to just solely individuals from Western Australia.

6.7 Conclusions

Sex is generally the first component of the biological profile estimated; it is

required to be highly accurate and reliable, as remaining biological profile

components (age, stature and ancestry) are often based on sex-‐specific standards.

The results of the present study clearly demonstrates that precise and accurate

linear measurements can be acquired from digital hand x-‐rays. Measurement

precision values (rTEM and R) for both acquisition methods tested (landmark and

line-‐tool method) were high and comparable to measurement precision results

from previously published sex estimation studies (e.g. Ishak et al. 2012; Franklin et

al. 2012b).

The present thesis has also demonstrated that the hand bones are sexually

dimorphic in a Western Australian population and can be used to classify sex with

a high degree of expected accuracy. Cross-‐validated classification accuracies range

between 76.7 to 91% for the 18 functions presented; the highest classification

accuracy resulting from a combination of eight measurements in the complete

hand. Although the majority of the functions presented had cross-‐validated

classification accuracies above 80%, not all were forensically applicable; functions

with a sex bias value greater than ±5% should only be used with due caution, as

CHAPTER SIX

89

they have a tendency to misclassify either males or females at disproportionate

levels.

The final objective of this thesis was to determine the minimum age at which sex

can be reliably estimated in the hand bones. A series of ANOVAS were performed

to determine the age at which hand bones were metrically sexually dimorphic.

Males were found to have larger hand bone measurements from approximately 14

to 15 years of age. However, sex could not accurately be estimated when applying

stepwise discriminant functions based on sub-‐adult (or adult) data, as sex bias

values were considerably high (ranging from 8.3 to 70%).

The present study has formulated population specific sexing standards based on

morphometric hand bone measurements for a Western Australian population;

such data did not previously exist. This represents a valuable addition to the

database of human identification protocols currently being developed as part of

on-‐going research projects at the Centre for Forensic Science at the University of

Western Australia. The standards presented here can be used to classify sex in

unknown remains in many different forensic and/or archaeological scenarios.

Importantly, these standards are based on data that is representative of the

contemporary Western Australian population and are thus the most appropriate

method of skeletal sex estimation using the hand bones.

91

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105

Appendix One

Table A1.1 Posterior probabilities calculated for the adult discriminant functions 1 to 18.

Posterior Probability intervals

Males

Females

n % n % Function 1 0.00-‐0.19

0 0

0 0

0.20-‐0.39 0 0 0 0 0.40-‐0.59 21 17.5 11 9.02 0.60-‐0.79

34 28.33

40 32.79

0.80-‐1.00 65 54.17

71 58.19 Function 2 0.00-‐0.19

0 0

0 0

0.20-‐0.39 0 0 0 0 0.40-‐0.59 13 11.02 10 8.26 0.60-‐0.79

32 27.11

29 23.97

0.80-‐1.00 73 61.87

82 67.77 Function 3 0.00-‐0.19

0 0

0 0

0.20-‐0.39 0 0 0 0 0.40-‐0.59 16 13.68 11 8.66 0.60-‐0.79

34 29.06

47 37.01

0.80-‐1.00 67 57.26

69 54.33 Function 4 0.00-‐0.19

0 0

0 0

0.20-‐0.39 0 0 0 0 0.40-‐0.59 9 8.18 16 13.33 0.60-‐0.79

29 26.36

33 27.5

0.80-‐1.00 72 65.46

71 60.17 Function 5 0.00-‐0.19

0 0

0 0

0.20-‐0.39 0 0 0 0 0.40-‐0.59 16 13.91 15 11.9 0.60-‐0.79

30 26.09

40 31.75

0.80-‐1.00 69 60

71 56.35

APPENDIX ONE

106

Posterior Probability intervals Males Females

n % n % Function 6 0.00-‐0.19

0 0

0 0

0.20-‐0.39 0 0

0 0

0.40-‐0.59 14 11.97

14 11.86

0.60-‐0.79 32 27.35

36 30.51

0.80-‐1.00 71 60.68 68 57.63 Function 7 0.00-‐0.19

0 0

0 0

0.20-‐0.39 0 0

0 0

0.40-‐0.59 5 4.03

6 4.51

0.60-‐0.79 22 17.74

18 13.53

0.80-‐1.00 97 78.23 109 81.96 Function 8 0.00-‐0.19

0 0

0 0

0.20-‐0.39 0 0

0 0

0.40-‐0.59 5 4

8 5.88

0.60-‐0.79 22 17.6

22 16.18

0.80-‐1.00 98 78.4 106 77.94 Function 9 0.00-‐0.19

0 0

0 0

0.20-‐0.39 0 0

0 0

0.40-‐0.59 12 10.08

8 6.4

0.60-‐0.79 21 17.65

25 20

0.80-‐1.00 86 72.27 92 73.6 Function 10 0.00-‐0.19

0 0

0 0

0.20-‐0.39 0 0

0 0

0.40-‐0.59 4 3.2

9 6.87

0.60-‐0.79 25 20

21 16.03

0.80-‐1.00 96 76.8 101 77.1 Function 11 0.00-‐0.19

0 0

0 0

0.20-‐0.39 0 0

0 0

0.40-‐0.59 11 8.87

4 3.08

0.60-‐0.79 19 15.32

24 18.46

0.80-‐1.00 94 75.81 102 78.46

APPENDIX ONE

107


n % n % Function 12 0.00-‐0.19 0 0 0 0 0.20-‐0.39

0 0

0 0

0.40-‐0.59 9 7.5

10 7.52

0.60-‐0.79 25 20.83

27 20.3

0.80-‐1.00 86 71.67 96 72.18 Function 13

0.00-‐0.19 0 0 0 0 0.20-‐0.39 0 0 0 0 0.40-‐0.59

6 4.55

3 2.13

0.60-‐0.79 5 3.79

12 8.51

0.80-‐1.00 122 91.73 127 89.44 Function 14

0.00-‐0.19

0 0

0 0

0.20-‐0.39 0 0

0 0

0.40-‐0.59 9 7.96 10 7.94 0.60-‐0.79 22 19.47 20 15.87 0.80-‐1.00 82 72.57 96 76.19 Function 15

0.00-‐0.19

0 0

0 0

0.20-‐0.39 0 0

0 0

0.40-‐0.59 4 3.22

10 7.35

0.60-‐0.79 15 12.1 21 15.44 0.80-‐1.00 105 84.68 105 77.21 Function 16 0.00-‐0.19 0 0 0 0 0.20-‐0.39

0 0

0 0

0.40-‐0.59 5 4

6 4.51

0.60-‐0.79 20 16

16 12.03

0.80-‐1.00 100 80 111 83.46 Function 17

0.00-‐0.19 0 0 0 0 0.20-‐0.39 0 0 0 0 0.40-‐0.59

9 7.26

7 5.26

0.60-‐0.79 10 8.06

12 9.02

0.80-‐1.00 105 84.68 114 85.72

APPENDIX ONE

108


n % n % Function 18 0.00-‐0.19 0 0 0 0 0.20-‐0.39

0 0

0 0

0.40-‐0.59 7 5.38

5 3.76

0.60-‐0.79 15 11.54

17 12.78

0.80-‐1.00 108 83.08 111 83.46

APPENDIX TWO

109

Appendix Two

Table A2.1 Unpaired t-‐test results for the comparison of metacarpal one, two and four lengths from the current study to four previously published studies.

Publication compared to current study

Measurementa Sex t Significance

(p) Scheuer & Elkington 1993

MLMC1 M 7.46 ***

F 4.68 ***

MLMC2 M 10.56 ***

F 7.16 ***

MLMC4 M 5.62 ***

F 2.44 **

Barrio et al. 2006 MLMC1 M 4.06 ***

F 5.34 ***

MLMC2 M 8.62 ***

F 10.14 ***

MLMC4 M 5.97 ***

F 7.01 ***

Case and Ross 2007 MLMC1 M 5.05 ***

F 5.28 ***

MLMC2 M 9.64 ***

F 8.73 ***

MLMC4 M 6.70 ***

F 5.59 ***

El Morsi & Hawary 2012 MLMC1 M 1.06 NS

F 0.09 NS

MLMC2 M 5.70 ***

F 7.50 ***

MLMC4 M 1.74 *

F 5.48 ***

Key: #Definition of measurements in Table 5.1; NS = not significant; * P<0.05, ** P<0.01, ***P<0.001

APPENDIX THREE

111

Appendix Three

Table A3.1 Unpaired t-‐test results for the comparison of males and females for each of the five comparative populations considered

Publication Measurementa t Significance (p) Current Study MLMC1 4.14 *** MLMC2 3.80 *** MLMC4 3.76 *** Scheuer and Elkington 1993

MLMC1 2.61 * MLMC2 3.19 **

MLMC4 1.96 ** Barrio et al. 2006 MLMC1 8.39 ***

MLMC2 7.97 ***

MLMC4 7.06 ***

Case and Ross 2007 MLMC1 10.36 ***

MLMC2 10.36 ***

MLMC4 9.71 ***


MLMC1 9.40 *** MLMC2 11.96 ***

MLMC4 13.61 ***


APPENDIX FOUR

113

Appendix Four Table A4.1 Descriptive statistics of mean sub-‐adult hand bone measurements (in mm) for Group A.

a Measurement Male (n = 10) Female (n=11) F R square p-‐value

Group A Mean SD Range Mean SD Range

MLMC1 45.2 1.94 42.38 -‐ 48.15 44.01 3.29 38.76 -‐ 49.21 0.98 0.05 NS WHMC1 14.16 1.64 11.66-‐16.58 13.48 0.56 12.48-‐14.70 4.62 0.08 * WBMC1 14.61 1.8 12.18-‐16.99 13.38 0.61 12.28-‐14.28 1.68 0.20 NS WMMC1 9.55 1.19 6.81-‐11.24 8.54 0.44 7.67-‐9.20 6.96 0.27 * MLMC2 68.06 3.76 63.17 -‐ 75.61 68.57 4.14 62.80 -‐ 76.00 0.09 0.00 NS WHMC2 13.99 2.12 11.68-‐17.39 14.08 1.48 11.14-‐16.40 0.08 0.00 NS WBMC2 18.59 1.81 16.43-‐21.13 18.28 2.99 15.18-‐26.59 0.01 0.00 NS WMMC2 8.34 1.32 6.83-‐10.44 7.3 0.49 6.20-‐7.98 5.98 0.24 * MLMC3 65.08 3.69 63.17 -‐ 75.61 63.57 3.6 58.61 -‐ 70.75 0.91 0.05 NS WHMC3 14.95 1.72 12.64-‐17.45 13.87 1.46 11.21-‐15.98 2.84 0.11 NS WBMC3 14.21 1.76 11.49-‐16.75 13.21 0.83 11.93-‐14.67 2.44 0.13 NS WMMC3 8.19 1.03 6.92-‐9.97 7.21 0.6 5.79-‐8.08 7.25 0.28 *

APPENDIX FOUR

114



MLMC4 57.15 3.21 52.36 -‐ 62.51 56.66 3.59 51.87 -‐ 65.21 0.11 0.01 NS WHMC4 12.64 1.68 9.93-‐15.16 11.36 0.9 9.40-‐12.60 1.02 0.20 NS WBMC4 12.54 1.52 10.92-‐14.93 11.99 0.93 10.22-‐13.68 4.83 0.05 * WMMC4 6.63 0.96 5.22-‐8.15 6.04 0.59 4.92-‐7.05 2.88 0.13 NS MLMC5 51.53 4.05 41.38-‐55.40 51.77 3.31 45.13-‐57.93 0.02 0.00 NS WHMC5 12.16 1.17 10.53-‐13.74 11.36 0.87 9.69-‐12.52 1.49 0.15 NS WBMC5 13.78 1.9 10.96-‐16.84 13.05 0.62 11.91-‐13.94 3.28 0.07 NS WMMC5 8.1 1.08 5.57-‐8.98 7.01 0.57 6.21-‐8.11 8.56 0.31 ** MLPP1 31.36 2.69 28.33-‐35.23 30.09 2.24 27.15-‐34.77 1.41 0.07 NS WHPP1 10.82 1.54 8.36-‐12.70 10.03 0.84 8.56-‐11.21 1.75 0.10 NS WBPP1 13.27 1.55 10.73-‐15.27 12.59 0.69 11.45-‐13.52 2.17 0.08 NS WMPP1 7.66 1.07 5.14-‐8.76 6.83 0.49 6.28-‐7.73 5.35 0.22 * MLPP2 40.05 1.98 36.89-‐42.49 39.83 2.31 36.35-‐43.02 0.05 0.00 NS WHPP2 10.68 1.05 8.96-‐11.82 10.46 0.41 10.04-‐11.53 2.26 0.02 NS WBPP2 15.28 1.31 13.71-‐17.94 13.98 0.57 13.09-‐15.14 0.42 0.11 NS WMPP2 9.1 1.02 7.54-‐10.73 8.17 0.6 7.34-‐9.25 6.6 0.26 * MLPP3 44.46 2.7 40.66-‐48.20 44.66 2.84 41.39-‐49.74 0.03 0.00 NS

APPENDIX FOUR

115



WHPP3 11.3 1.44 8.99-‐13.18 11.29 0.58 10.67-‐12.46 9.63 0.00 ** WBPP3 15.32 1.3 13.45-‐17.92 13.98 0.57 13.09-‐15.14 0 0.34 NS WMPP3 9.33 0.94 7.91-‐10.78 8.34 0.64 7.26-‐9.29 8 0.30 * MLPP4 42.04 2.07 39.23-‐45.28 41.03 2.52 37.25-‐45.15 1.01 0.05 NS WHPP4 10.26 1.36 7.39-‐11.85 10.36 0.66 9.27-‐11.43 4.47 0.00 * WBPP4 13.74 1.09 12.00-‐15.95 12.93 0.62 11.51-‐13.48 0.04 0.19 NS WMPP4 8.61 0.87 6.92-‐9.98 7.57 0.62 6.74-‐8.38 9.99 0.34 ** MLPP5 32.84 1.95 29.74-‐35.50 32.19 1.82 30.31-‐35.35 0.63 0.03 NS WHPP5 8.92 1.18 6.74-‐10.29 8.35 0.26 7.88-‐8.65 1.67 0.11 NS WBPP5 13.03 1.2 10.84-‐14.43 12.52 0.51 11.34-‐13.13 2.46 0.08 NS WMPP5 7.1 0.92 5.40-‐8.67 6.41 0.78 5.42-‐7.65 3.42 0.15 NS


APPENDIX FOUR

116

APPENDIX FOUR

117

Table A4.2 Descriptive statistics of mean sub-‐adult hand bone measurements (in mm) for Group B.


Group B Mean SD Range Mean SD Range

MLMC1 47.53 3.11 42.72 -‐ 55.61 44.24 2.68 38.88 -‐ 50.16 12.83 0.25 *** WHMC1 15.11 1.59 12.41 -‐ 17.59 13.33 1.42 11.89 -‐ 17.12 40.89 0.12 *** WBMC1 14.74 1.01 13.72 -‐ 17.34 13.67 0.73 11.76 -‐ 14.64 5.04 0.52 * WMMC1 9.63 1.28 7.65 -‐ 12.34 8.69 0.87 7.20 -‐ 10.76 7.24 0.16 * MLMC2 70.52 4.69 61.41 -‐ 79.52 68.48 3.84 59.40 -‐ 74.89 2.27 0.06 NS WHMC2 19.29 1.74 12.38 -‐ 18.87 17.19 1.33 10.98 -‐ 15.73 24.66 0.16 *** WBMC2 15.42 1.61 16.09 -‐ 22.33 14.10 1.00 14.42 -‐ 18.78 7.19 0.39 * WMMC2 8.82 0.95 6.94 -‐ 10.48 7.71 0.71 6.70 -‐ 9.50 17.61 0.32 *** MLMC3 66.19 4.46 59.82 -‐ 74.08 63.64 3.76 55.68 -‐ 70.10 3.83 0.09 NS WHMC3 14.61 1.26 13.97 -‐ 18.67 13.34 1.30 11.83 -‐ 16.44 13.78 0.30 *** WBMC3 16.19 1.06 12.98 -‐ 16.57 14.55 1.09 11.61 -‐ 15.04 16.32 0.27 *** WMMC3 8.47 0.62 7.57 -‐ 9.63 7.59 0.62 6.72 -‐ 8.64 20.35 0.35 *** MLMC4 58.92 3.81 53.43 -‐ 65.69 56.22 3.31 49.74 -‐ 62.98 5.72 0.13 * WHMC4 13.00 0.98 11.74 -‐ 15.42 11.89 0.93 9.90 -‐ 13.46 14.28 0.39 *** WBMC4 13.56 1.07 11.82 -‐ 15.66 12.07 0.77 10.50 -‐ 13.46 24.19 0.27 ***

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WMMC4 7.25 0.82 5.98 -‐ 8.87 6.10 0.43 5.35 -‐ 6.89 30.64 0.45 *** MLMC5 53.67 4.21 47.9 -‐ 62.93 51.47 2.88 45.6 -‐ 56.65 3.73 0.09 NS WHMC5 13.92 1.11 11.16 -‐ 14.89 12.84 1.01 8.54 -‐ 12.84 11.10 0.29 ** WBMC5 12.85 1.21 11.28 -‐ 16.06 11.54 0.81 11.17 -‐ 14.21 15.29 0.23 *** WMMC5 8.37 0.84 6.49 -‐ 9.92 7.08 0.75 5.91 -‐ 8.60 25.97 0.41 *** MLPP1 33.12 2.49 28.63 -‐ 37.55 30.48 1.70 27.18 -‐ 34.35 15.29 0.29 *** WHPP1 14.08 1.15 8.22 -‐ 12.98 12.80 1.02 8.26 -‐ 11.84 9.46 0.31 ** WBPP1 11.38 1.52 11.06 -‐ 16.2 9.96 1.09 10.99 -‐ 14.53 16.83 0.20 *** WMPP1 7.80 0.84 5.82 -‐ 9.2 6.92 0.69 5.43 -‐ 7.71 12.99 0.25 *** MLPP2 41.23 2.99 36.08 -‐ 46.55 39.89 2.52 34.86 -‐ 45.81 2.36 0.06 NS WHPP2 15.95 0.96 9.83 -‐ 13.19 14.74 0.81 8.38 -‐ 12.39 21.02 0.16 *** WBPP2 11.35 0.83 14.23 -‐ 17.3 10.58 0.84 12.31-‐ 16.03 7.46 0.36 ** WMPP2 9.58 0.93 7.3 -‐ 11.14 8.40 0.82 7.01 -‐ 11.05 17.98 0.32 *** MLPP3 46.50 3.06 41.61 -‐ 51.58 44.43 2.94 39.14 -‐ 50.93 4.77 0.11 * WHPP3 15.72 1.19 9.7 -‐ 14.79 14.39 0.72 9.53 -‐ 12.37 25.54 0.17 *** WBPP3 12.03 0.83 14.48 -‐ 17.43 11.17 0.83 11.86 -‐ 15.29 7.84 0.40 ** WMPP3 9.90 0.98 7.81 -‐ 11.8 8.40 0.64 7.24 -‐ 10.49 33.09 0.47 ***

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MLPP4 43.12 3.05 38.05 -‐ 48.83 41.46 2.51 36.68 -‐ 48.39 3.52 0.08 NS WHPP4 14.33 1.17 9.32 -‐ 14.04 13.11 0.77 8.13 -‐ 11.94 20.69 0.13 ***

WBPP4 11.19 0.97 12.28 -‐ 16.31 10.45 0.72 11.29 -‐ 14.15 5.54 0.35 * WMPP4 9.05 1.07 6.9 -‐ 11.64 7.71 0.66 6.73 -‐ 9.85 22.52 0.37 *** MLPP5 33.80 2.96 27.96 -‐ 39.1 32.30 2.11 29.21 -‐ 37.63 3.41 0.08 NS WHPP5 13.46 0.87 7.2 -‐ 10.64 12.73 0.76 6.63 -‐ 10.02 8.20 0.15 ** WBPP5 9.12 0.94 11.07 -‐ 14.91 8.46 0.64 10.94 -‐ 13.73 6.60 0.18 * WMPP5 7.49 1.08 5.56 -‐ 9.46 6.29 0.60 5.15 -‐ 7.76 18.97 0.33 ***


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Table A4.3 Descriptive statistics of mean sub-‐adult hand bone measurements (in mm) for Group C.

a Measurement Male (n = 20) Female (n=19) F R square p-‐value Group C Mean SD Range Mean SD Range

MLMC1 48.58 2.78 45.61 -‐ 54.18 45.14 2.47 40.34 -‐ 48.77 9.84 0.43 ** WHMC1 16.17 1.28 14.53 -‐ 19.1 13.64 0.96 11.68 -‐ 15.42 32.95 0.50 *** WBMC1 15.86 1.23 14.29 -‐ 18.46 13.92 0.79 12.6 -‐ 15.3 11.42 0.59 ** WMMC1 9.98 0.88 7.94 -‐ 11.3 8.49 0.65 7.65 -‐ 9.62 21.56 0.45 *** MLMC2 75.54 4.33 66.97 -‐ 81.48 68.44 3.61 61.47 -‐ 77.41 16.15 0.48 *** WHMC2 20.72 1.18 14.93 -‐ 18.93 17.40 0.84 12.85 -‐ 15.52 45.80 0.57 *** WBMC2 16.91 1.35 18.41 -‐ 23 14.24 1.04 15.04 -‐ 18.8 37.14 0.60 *** WMMC2 9.15 0.86 7.82 -‐ 11.14 7.93 0.57 6.93 -‐ 9.16 19.94 0.52 *** MLMC3 70.17 4.21 61.9 -‐ 79.21 63.73 3.69 56.25 -‐ 72.01 15.39 0.45 *** WHMC3 15.39 1.30 14.98 -‐ 20.14 13.74 1.09 13.04 -‐ 16.71 9.61 0.53 ** WBMC3 17.60 1.14 13.73 -‐ 18.79 15.00 0.73 12.67 -‐ 15.33 35.33 0.38 *** WMMC3 8.93 0.81 8.02 -‐ 11.08 7.88 0.63 7.06 -‐ 9.28 11.46 0.38 ** MLMC4 61.52 3.48 55.47 -‐ 68.32 55.91 3.34 48.18 -‐ 61.58 17.70 0.48 *** WHMC4 14.64 1.16 13 -‐ 17.24 12.26 1.06 10.58 -‐ 15.21 24.54 0.47 *** WBMC4 14.85 1.22 12.55 -‐ 17.52 12.80 0.64 11.28 -‐ 13.55 14.03 0.45 *** WMMC4 7.31 0.63 6.54 -‐ 8.35 6.68 0.73 5.38 -‐ 7.99 3.35 0.29 NS

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MLMC5 55.38 3.32 49.78 -‐ 61.31 52.01 2.43 47.39 -‐ 55.81 8.71 0.33 ** WHMC5 14.80 0.87 12.87 -‐ 16.14 13.19 0.74 11.52 -‐ 14.44 28.44 0.57 *** WBMC5 14.22 0.99 12.45 -‐ 16.3 12.52 0.74 11.77 -‐ 14.37 20.75 0.37 *** WMMC5 8.81 0.92 7.19 -‐ 10.58 7.42 0.57 6.19 -‐ 8.1 17.76 0.43 *** MLPP1 34.31 2.46 32 -‐ 39.69 31.55 1.75 27.29 -‐ 33.34 10.40 0.46 ** WHPP1 14.62 1.10 9.84 -‐ 13.87 12.50 0.79 8.32 -‐ 11.69 19.67 0.41 *** WBPP1 11.63 1.01 12.53 -‐ 16.7 10.06 1.03 10.06 -‐ 14.15 10.06 0.49 ** WMPP1 7.81 0.77 6.37 -‐ 9.4 6.62 0.77 5.34 -‐ 8.25 9.35 0.33 ** MLPP2 43.08 2.95 37.47 -‐ 48.03 40.03 1.63 35.95 -‐ 42.41 9.07 0.31 ** WHPP2 17.31 0.68 10.85 -‐ 13.33 15.10 0.62 9.29 -‐ 12.08 18.70 0.63 *** WBPP2 12.45 1.12 14.97 -‐ 20.3 10.52 0.92 13.49 -‐ 18.08 48.54 0.52 *** WMPP2 10.16 1.01 8.52 -‐ 11.91 8.60 0.64 7.35 -‐ 9.8 16.64 0.40 *** MLPP3 48.07 3.39 42.81 -‐ 54.16 44.77 2.09 40.86 -‐ 48.51 6.05 0.31 * WHPP3 16.98 0.81 10.93 -‐ 14.52 14.57 0.61 10.37 -‐ 12.9 21.06 0.61 *** WBPP3 13.19 1.28 13.97 -‐ 19.37 11.34 0.90 13.54 -‐ 17.55 28.99 0.48 *** WMPP3 10.26 0.99 8.39 -‐ 12.28 9.03 0.63 7.39 -‐ 9.83 12.31 0.34 ** MLPP4 45.55 3.64 39.54 -‐ 51.61 41.74 1.73 38.25 -‐ 44.22 8.56 0.36 **

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WHPP4 15.70 0.76 10.01 -‐ 13.28 13.58 0.65 9.87 -‐ 12.97 20.99 0.52 *** WBPP4 12.17 1.22 12.68 -‐ 18.58 10.64 0.63 12.8 -‐ 15.04 19.64 0.43 *** WMPP4 9.53 0.92 7.26 -‐ 10.49 8.10 0.59 7.11 -‐ 8.94 23.65 0.40 *** MLPP5 35.02 2.71 30.82 -‐ 41.46 32.78 1.50 29.37 -‐ 34.9 7.10 0.35 * WHPP5 14.61 0.74 8.66 -‐ 11.61 12.85 0.61 7.58 -‐ 10.23 43.66 0.50 *** WBPP5 10.20 0.76 12.3 -‐ 15.7 8.75 0.51 11.95 -‐ 14.15 25.67 0.60 *** WMPP5 7.71 0.96 5.73 -‐ 8.95 6.71 0.62 5.4 -‐ 7.85 8.21 0.30 *


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