Experimental, Quasi-experimental, and Single Subject Research

774/801 Sept 1, 2004

John Hattie & Tony Hunt

It is simple: There is no perfect experiment

in education

There is nearly always a trade off between the

Power to generalise - from sample to population- from items to the behaviour domain- from conditions in the study to all intended conditions

and the

Power to convince- there are many audiences

PG PC

Power to Generalise

How confident can be generalise from the study to all “similar” situations

Is the design replicable/reproducible/exchangeable?

Is the evidence/conclusions unique to this study?

Have the generalisations taken into account all possible competing views – plausible alternative rival explanations (PARE)

PG

PC

Power to convince

Who are we trying to convince If it is a colleague(s) then more situation

specificity may be convincing (kids/classrooms/schools like mine)

If it is the educational community, then situation needs to be less critical

PC

PG

Resolution: Linking Power

Experimental design consists of a series of links:– It is as strong as the weakest link– Each link influences the next link– Desirable to have equal strength– Does each link have explanatory power– Are conclusions credible to the intended audience

History

Stanley and Campbell (1963) Cook and Campbell (1979) Shadish, Cook & Campbell (2002)

Evidence based

All based on designing studies that can lead to explanation and claims of causality

Explanation and Cause

1. Cause and effect must be related

(e.g., self-concept & achievement)

1. There needs to be temporal order (cause before effect)

2. Need to rule out other explanations/ other Plausible Alternative Rival Explanations (PARE)

Campbell & Stanley (1963)

Pretest-Posttest Control Group Design

Pre Treatment Post

R O X O

R O O

Randomisation – aiming for representativeness

But can we randomise

No Child Left Behind Tennessee Class Size Study

Quasi-experimentation:

When you do not have so much control over allocation of treatment, conditions, sample

When you have non-equivalent groups

In quasi-experimentation, the researcher has to enumerate alternative explanations one by one, decide which are plausible, and then use logic, design, and measurement to assess whether each one is operating in a way that might explain any observed effect (Shadish, Cook & Campbell, 2002, p. 14)

Relates to the Popper notion of falsification: What evidence would you accept that you are wrong?

Examples of Quasi-experimental designs

Divorce LawsOzdowski, S.A. & Hattie, J.A. (1981).

The impact of divorce laws on divorce rate in Australia: A time series analysis. Australian Journal of Social Issues, 16, 3-17.

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a. Time SeriesO1 O2 O3 O4 O5 X O6 O7 O8

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ABA designA B A

O1 O2 O3 X4 X5 X6 O7 O8 O9 Le Fevre, et.al. (2002). Adequate Decoders

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BLine 1 BLine 2 Intervention FollowUp

Manitenance

Daily Mean

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Multilevel Design: Hierarchical Linear Modelling

Students within classes within schools

E.g., Tracking/Streaming

School 1 School 2…

Teacher 1 Teacher 2

Class 1 Class 2 Class 1 Class 2

Structural Equation Modelling

Nutrition

nutr138e5

.74nutr123e4

.78nutr120e3.84

Exercise

exer53

e6

.62exer54e7

.54exer55e8

.85

Self Care

selfc59

e14

.57

selfc58

e13

.79

selfc57

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.66

selfc56

e11

.68

exer131e9 .88

exer139e10

.76

nutr51e2 .68

nutr50e1

.84

.79

.69

.80

Structural Model for Physical Self

Social

jfriend e15.97

jlove e16

.92

Interactive

jintell e17

.93

jwork e18.89

jemot e19.92

jcontr e20

.94

jhumor e21.86

-.23

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.60

e22

e23

-.14.55

.67

Minimal requirements for Studies

Sampling– Items to behaviour domains– People to all possible people– Conditions to all possible conditions

Representative sampling via– Random sampling– Stratified random sampling

Variables

At the end of your study, can I say “Aha, so that is what you mean, now I am clear”

Open constructs NOT Definitions No such thing as immaculate perception Dependent - Manipulable Independent - Nonmanipulable

Dependability

How reliable/consistent/replicable are your measures/ observations

Validity = Interpretations

Validity - "an integrated evaluative judgement of the degree to which empirical evidence and theoretical rationales support the adequacy and appropriateness of inferences and actions based on test scores or other modes of assessment".

Not validity of a test, but validity of interpretations

Validity of your study …

Is related to having ruled out

Plausible Alternative Rival Explanations (PARE)

CONTROL CONTROL CONTROL

… some examples

1. PARE: Power

Is your study POWERFUL enough to detect the effect you are investigating

1. PARE: Power

Is your study POWERFUL enough to detect the effect you are investigating

Do chickens have lips?

2. PARE: Chance

Did the effect/conclusion occur by chance

E.g., That two means are the same – the

hypothesis of no difference

Setting a rejection level, say =.05

3. PARE: Type II errors

Type I errors – Rejecting a claim when it is true (=.05)

Type II errors – Accepting a claim when it is false (e.g., chickens do not have lips, if it is indeed true)

4. PARE Reliability of your measures

If the reliability is low, then the scores “wobble” and no guarantee you will get same results using these instruments (tests, observations, interviews, etc.)

Was the treatment “consistent” in the various classes/implementations?

5. PARE: Was the treatment implemented?

Degree of implementation The Hong Kong Practical Science Study

(Cheung, Hattie, & Bucat, 1997)

6. PARE: Maturation

Showing change may not be enough as kids improve anyway (e.g., by maturation)

Method to measure change = Effect-sizesPost-Pre/spread = Effect-size

X2 – X1 sddiff

e.g., Before = 12, After = 15, spread = 6 15-12 = .5

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Distribution of effects

Zero achievement

Average effect

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Distribution of effects

Maturation

The disasters …

71 programmed instruction 801 .14

72 finances 1634 .14

73 problem based learning 41 .12

74 diet 255 .12

75 gender (female-male) 9020 .09

76 inductive teaching 570 .06

77 team teaching 41 .06

78 ability grouping 3355 .05

79 class size 2559 .05

80 open vs. traditional 3426 -.01

81 summer vacation 269 -.06

82 retention 3626 -.17

83 transfer of school 354 -.26

84 disruptive students 1511 -.78

The also rans …56 metacognitive intervention 921 .29

57 math programs 3326 .27

58 audio-visual 2699 .26

59 gifted programs 47 .25

60 coaching 1076 .24

61 behavior objectives 157 .24

62 calculators 238 .24

63 mainstreaming 1641 .21

64 questioning 493 .20

65 learning hierarchies 168 .19

66 attitude to math 1122 .19

67 desegregation 1590 .18

68 play 129 .16

69 television 4337 .15

Almost there …

42 tutoring 136 .35

43 activity-based programs 674 .35

44 remedial programs 1438 .35

45 classroom climate 2726 .35

46 social skills training 5472 .35

47 time 1680 .34

48 CAI 18231 .32

49 inquiry based teaching 2740 .32

50 preschool 242 .32

51 whole language 198 .31

52 within class grouping 2359 .31

53 testing 1463 .31

54 problem solving 1141 .30

55 background 692 .30

In the middle …

29 parent involvement 2597 .46

30 bilingual programs 1501 .46

31 adjunct aids 659 .45

32 concept mapping 18 .45

33 advance organizers 2106 .44

34 hypermedia instruction 317 .44

35 socio economic status 1657 .44

36 perceptual-motor skills 7592 .42

37 individualised instruction 5948 .42

38 homework 568 .41

39 competitive learning 144 .41

40 simulations 972 .37

41 expectations 912 .36

Worth having …

14 self-assessment 152 .54

15 mastery learning 1933 .53

16 creativity programs 2340 .52

17 interactive video 1152 .52

18 psycho-linguistics 4404 .51

19 goals 959 .51

20 peer influence 366 .50

21 early intervention 30971 .49

22 outdoor education 294 .49

23 science 4124 .49

24 inservice ed 18644 .48

25 acceleration 371 .47

26 motivation 2196 .47

The MAJOR Influences …

Influence # effects Mean

1 Direct instruction 1925 .93

2 Reciprocal teaching 52 .86

3 Feedback 13209 .81

4 Cognitive strategy training 7649 .80

5 Classroom behaviour 361 .71

6 Prior achievement 2094 .71

7 Phonological awareness 2630 .70

8 Home encouragement 25706 .69

9 Piagetian programs 786 .63

10 Cooperative learning 1153 .59

11 Reading programs 14945 .58

12 Quality of teaching 808 .55

13 Study skills 3224 .54

Identifying that what matters

Percentage of Achievement Variance

Students

Teachers

Home

PeersSchools Principal

7. PARE: Testing

People become test wise and/or may respond different when under test conditions

White space and testing in asTTle Testwiseness

Test of Objective EvidenceEach of the questions in the following set has a logical or

"best" answer from its corresponding multiple choice answer set. Please record your eight answers.

1. The purpose of the cluss in 2 Trassig is true when furmpaling is to removeA clump trasses the vonA cluss-prags B the viskal flans, if the viskal is B tremails donwil or zortilC cloughs C the belgo frulsD pluomots D dissels lisk easily

3 The sigia frequently overfesks the 4. The fribbled breg will minter best trelsum because with an

A all sigias are mellious A derstB all sigias are always votial B morstC the trelsum is usually tarious C sortarD no trelsa are feskable D ignu

Test of Objective Evidence, Part II

5 The reasons for tristal doss are 6 Which of the following is/are always present when trossels

are being gruven?

A the sabs foped and the doths tinzed A rint and vostB the dredges roted with the crets B vostC few rakobs were accepted in sluth C shum and vostD most of the polats were thonced D vost and plone

7 The mintering function of the ignu is most 8effectively carried out in connection with

A a razma toi AB the groshing stantol BC the fribbled breg CD a frailly sush D

8. PARE Statistical Regression

When taking extreme groups the means tend to move to the middle.

Why do the tallest fathers have shorter sons, and the shortest fathers have taller sons?

…. Regression to the Mean

Special Education (e.g., Sesame Street) Effective schools Gifted education

9. PARE Response rates

The returns of questionnaires/tests/interviews

should be high

What is typical?

Meta-analyses of Response Rates

Typical return is 50%

Three major factors:

1. Salience (77% vs 42%)

2. Number of follow ups (halve each time)

3. Lack of clutter/ orderliness

Not length (ave 7 pages, 72 questions), colour,

10. Change scores

The difference between post-pre scores

Problems

1 Unreliable

2 Are you measuring same thing both times

3 Regression to the mean

11 PARE: Experimenter effects

Hawthorne effect: Because we know we are in an experiment this alters our responses

Hans the Horse Pygmalion in the classroom Christine Rubie’s thesis Stanley Milgrim’s experiment

12. PARE: Restriction of range

When you choose/focus on a narrow range of abilities (etc.) this can be misleading

Picture …

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13. PARE: Specification of target and accessible sample/population

Most experiments are highly local but have general aspirations

Often, there are two groups you are generalising to: e.g., all secondary students in NZ, and to all secondary students you have access -- from which to sample

14: Interactions

The model of individual differences indicates that we should modify our teaching methods to allow for individual differences in the class

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Competitive Learning

Cooperative Learning

Girls Boys

The art of research design is to devise experiments to identify the explanation and cause of effects – by

Maximising the chance that the conclusions are defensible and

Minimising the PAREs

Such that you have

Power to Generalise and

Power to Convince

Unobtrusive measures

Which painting do most people watch? Friendship in cities Racism in suburbs/cities

Statistical Methods to assist …

Correlation Analysis of variance (anova) Cross-tabulation

Comparing means: Magnitude and Chance

Magnitude Effect-sizes Chance Analysis of Variance

Well-being

What are the differences in levels of WELL-BEING among males and females, and between Australia and New Zealand

Country * GENDER Cross tabulationCount GENDER

MALE FEMALE Total

Country New Zealand 516 644 1160Australia 421 694 1115

Total 937 1338 2275

Australia Mn sd Effect-size

Male 45.7 10.6

Female 46.2 10.6 0.04

Total 46.0 10.6

New Zealand

Male 53.6 7.5

Female 54.3 7.4 0.08

54.0 7.5

NZ - Australia 0.89

anova

Source df MS F p

Country 1 35211.9 416.71 <.001

Gender 1 151.8 1.80 .180

Country * Gender 1 6.1 0.07 .787

Error 2271 84.5