Person Target Profiling

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Person-Target Profiling

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Paul Barrett

Within many domains of enquiry or activity it is important to be able to describe

features of individuals associated with an activity or behaviour which contribute to

some criterion of current or future success or failure. The span of these domains is

wide, encompassing areas such as the assessment of risk of recidivist violence

(Webster, Harris, Rice, Cormier, & Quinsey, 2004; Monahan, Steadman,Appelbaum, Robbins, Mulvey, Silver, Roth, & Grisso, 2000), clinical

psychopathology profiles (Groth-Marnat, 2003), career suitability (Peterson,

Mumford, Borman, Jeanneret, Fleishman, Levin, Campion, Mayfield, Morgeson,

Pearlman, Gowing, Lancaster, Silver, and Dye, 2001), job success via personality

and intelligence scores (Schmidt and Hunter, 1998, 2004), consumer shopping

preference (Dickson, 2001; Taylor and Cosenza, 2002), and internet consumer

advertising (Raghu, Kannan, Rao, and Winston, 2001). Within all these domains, the

aim is not just to describe the differentiating features of individuals or groups, but to

incorporate the knowledge gained into a predictive framework or system which will

permit future individuals to be classified, ranked, scored, or chosen/rejected

according to their relative standing on those feature attributes.

This chapter seeks to describe the substantive logic, methods, coefficients, and

comparison procedures most commonly used within the domain of person-target

profiling. These generally fall into methods of constructing prototype score vectors

that are definitional for some preferred criterion, then using these score vectors astargets against which new individuals may be compared. Hence, much of this work

is concerned with constructing score vectors and with choosing or designing the

comparison coefficients which will be used to represent target profile disparity.

However, with the advent of a new class of algorithmic data methods into statistical

science, a significant part of this chapter is devoted to explaining what they are, and

demonstrating their likely superior utility within a typical profiling application. The

reason these methods are referred to as algorithmic is because they tend to work

entirely within a framework of maximising the predictive or profile classification

accuracy, not by assuming a standard probabilistic linear model for the data, but by

Book Chapter:

Barrett, P.T. (2005) Person-Target Profiling. In Andr Beauducel,Bernhard Biehl, Michael Bosnjak, Wolfgang Conrad, Gisela Schnberger,and Dietrich Wagener (Eds.) Multivariate Research Strategies: aFestschrift for Werner Wittman. Chapter 4, pp 63-118. Aachen: Shaker-Verlag.


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1=

n

i

i

x

Elevation xn

=

=

(1)

2

1

( )

=

n

i

i

x x

Scatter nn

=

-

(2)

( )= i

x xShape

Scatter

-

(3)

To show the effect of these transformations, let us look at two score profiles, one

designated as a target profile the other as a comparison profile. Figure 2 displays theraw profiles.

Figure 2: Two profiles of scale scores

What we see is that the two profiles look the same shape (more or less) but are

different in that the target scores are all lower than the other set of scale scores. If we

subtract each profiles elevation parameter from the respective profiles, the results

can be seen in Figure 3.


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Figure 3: Two profiles of scale scores with their respective elevation (means) subtracted

Now the profiles seem to be much closer to each other - centered as they are

around each of their respective means. What has happened is that we have removed

the average "elevation" from each profile - so that each may be expressed in a

common metric that always possesses a mid-point (average transformed data mean)

of 0. Figure 4 shows the effect of equating for the variability associated with each

profile. The shape, accentuation, or pattern, has been retained, but we have equated

profiles for "level" and "variability".


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TLabelY

BLabelY

Scale Low Score Desc Coeff. = 0.6 High Score Desc.

fA Cool Reserved Outgoing

Low Intellectance High Intellectance

fC Affected By Feelings Emotionally Stable

fE Accommodating Dominant

fF Sober Serious Enthusiastic

fG Expedient Conscientious

fH Retiring Socially Bold

fI Tough Minded Tender Minded

fL Trusting Suspicious

fM Practical Abstract

fN Forthright Discreet

fO Self-Assured Apprehensive

fQ1 Conventional Radical

fQ2 Group-Orientated Self-Sufficient

fQ3 Undisciplined Self-Disciplined

fQ4 Relaxed Tense-Driven

1 2 3 4 5 6 7 8 9 10

15FQ+ Profile

Figure 5: A Person-Target profile matching display, comparing the authors test scores (black line)

to those of the target profile (grey line) of a small group of New Zealand Executive Business

students, using the Psytech International 15FQ+ personality test within the GeneSys Profiler

module.

An example of a two-dimensional profile is taken from the StaffCV Inc.

(www.staffcv.com) job-preference mapping system. This unique profiling application

uses two dimensions in which individuals rate their preference for work-behaviour

attributes whilst simultaneously indicating their preferred frequency for engaging in

these behaviours. The profiles for IT helpdesk/technical support staff is shown in

Figure 6. The full measurement range is between 0 to 100 on each axis, but for clarity

the zoomed profile display are shown.


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Figure 6: Help Desk/Technical Support staff, 2-dimensional work behaviour attribute target profile.

Finally, an example of a possible model for a three dimensional profile is

provided in Figure 7. This profile is intrinsically non-linear. What it depicts is a three

dimensional profile for a single attribute (say working alone), where the preference

for that attribute (magnitude preference), the amount one wishes to engage in doing it

(frequency), and the weight by which departures from an optimal comparative

match are multiplied, are represented in three-dimensional space.


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Person Target Profiling

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