Brian LukoffStanford UniversityOctober 13, 2006

Based on a draft paper that is joint work with Eric Heggestad, Patrick Kyllonen, and Richard Roberts

The decision tree method and its applications to faking

Evaluating decision tree performance Three studies evaluating the method

Study 1: Low-stakes noncognitive assessments

Study 2: Experimental data Study 3: Real-world selection

Implications and conclusions

A technique from machine learning for predicting an outcome variable from (a possibly large number of) predictor variables

Outcome variable can be categorical (classification tree) or continuous (regression tree)

Algorithm builds the decision tree based on empirical data

Is it snowing?

Is it raining?drive

drive walk

Yes No

Yes No

Day Snowing?

Raining?

Method

1 yes yes drive2 yes no drive3 no yes drive4 no yes walk5 no no walk6 no no walk7 no yes drive

TRAINING SET

Is it snowing?

Is it raining?drive

drive walk

Yes No

Yes No

Day

Snowing?

Raining?

Method

1 yes yes drive2 yes no drive3 no yes drive4 no yes walk5 no no walk6 no no walk7 no yes drive Not all cases are accounted for correctly

Wrong decision on Day 4 Need to choose variables predictive enough

of the outcome

TRAINING SET

Is it snowing?

Is it raining?drive

drive walk

Yes No

Yes No

Not all cases are predicted correctly Maybe the decision to drive or walk is

determined by more than just the snow and rain?

Day

Snowing?

Raining?

Method

Prediction

8 yes yes drive drive9 no yes walk drive10 no yes drive drive11 yes no drive drive12 no no walk walk13 no no walk walk14 yes yes drive drive

TEST SET

Ease of interpretationSimplicity of useFlexibility in variable selectionFunctionality to build decision trees

readily available in software (e.g., the R statistical package)

Outcome variable = faking status (“faking” or “honest”) Training set = an experimental data set

where some participants instructed to fake Training set = a data set where some

respondents are known to have faked Outcome variable = lie scale score

Training set = a data set where the target lie scale was administered to some subjects

So far, have used individual item responses only

Other possibilities: Variance of item responses Number of item responses in the highest

(or lowest category) Modal item response

Decision tree method permits some sloppiness in variable selection

Classification trees (dichotomous outcome case, e.g., predicting faking or not faking) Accuracy rate False positive rate Hit rate

Continuous Average absolute error Correlation between actual and predicted

scores

Algorithm can “overfit” to the training data, so performance metrics computed on the training data not indicative of future performance

Thus we will often partition the data: Training set (data used to build tree) Test set (data used to compute

performance metrics)

Training/test set split leaves a lot to the chance selection of the training and test set

Instead, partition the data into k equal subsets Use each subset as a test set for the tree

trained on the rest of the data Average the resulting performance metrics to

get better estimates of performance on new data

Here we will report cross-validation estimates

Data sets Two sets of students (N = 431 and N = 824) that took a

battery of noncognitive assessments as well as two lie scales as part of a larger study

Measures Predictor variables▪ IPIP (“Big Five” personality measure) items▪ Social Judgment Scale items

Outcomes (lie scales)▪ Overclaiming Questionnaire▪ Balanced Inventory of Desirable Responding

Method Build regression trees to predict scores on each lie scale

based on students’ item responses

Varying performance, depending on the items used for prediction and the lie scale used as the outcome

Correlations between actual lie scale scores and predicted scores ranged from -.02 to .49

Average prediction errors ranged from .74 to .95 SD

Low-stakes setting: how much faking was there to detect?

Nonexperimental data set: students with high scores on the lie scales may or may not have actually been faking

Data sets An experimental data set of N = 590 students in two

conditions (“honest” and “faking”) Measures

Predictor variables▪ IPIP (“Big Five” personality assessment) items

Method Build decision trees to classify students as honest or

faking based on their personality test item responses

Decision trees correctly classified students into experimental condition with varying success Accuracy rates of 56% to 71% False positive rates of 25% to 41% Hit rates of 52% to 68%

Two items on a 1-5 scale form a decision tree: Item 19: “I always

get right to work” Item 107: “Do

things at the last minute” (reversed)

Extreme values of either one are indicative of faking

Many successful trees utilized few item responses

Range of tree performance Laboratory—not real-world—data Although an experimental study, still don’t

know: If students in the faking condition really faked If the degree to which they faked is indicative of

how people fake in an operational setting If any of the students in the honest condition

faked

Data set N = 264 applicants for a job

Measures Predictor variables▪ Achievement striving, assertiveness, dependability,

extroversion, and stress tolerance items of the revised KeyPoint Job Fit Assessment

Outcome (lie scale)▪ Candidness scale of the revised KeyPoint Job Fit Assessment

Method Build decision trees predicting the candidness (lie scale)

score from the other item responses

Correlations between actual and predicted candidness (lie scale) scores ranged from .26 to .58

Average prediction errors ranged from .61 to .78 SD

Items are on a 1-5 scale, where 5 indicates the highest level of Achievement Striving

Note that most tests are for extreme item responses

Similar methodology to Study 1, but better results (e.g., stronger correlations)

Difference in results likely due to the fact that motivation to fake was higher in this real-world, high-stakes setting

Wide variety in decision tree quality between groups of variables (e.g., conscientiousness scale vs. openness scale)

Examining trees can give insight into the structure of the assessment

Some decision trees in each study used only a small number of items and achieved a moderate level of accuracy

Use decision trees for real-time faking detection on computer-administered noncognitive assessments

Real-time “warning” system Need to study how this changes the

psychometric properties of the assessment

Address whether decision trees can be effective in an operational setting—are current decision trees accurate enough to reduce faking?

Comparisons of decision tree faking/honest classification with classifications from IRT mixture models

Develop additional features to be used as predictor variables

Explore other machine learning techniques