CAT Item Selection and Person Fit: Predictive Efficiency and Detection of Atypical Symptom Profiles Barth B. Riley, Ph.D., Michael L. Dennis, Ph.D., Kendon J. Conrad, Ph.D. Funded by NIDA grant 1R21DA025731

IntroductionIntroduction Do our measures accurately reflect a person’s performance or status? –Example: Persons with few endorsed symptoms, but symptoms of high severity Person fit statistics offer a means of detecting these patterns. But, detecting person misfit in CAT is problematic: –Reduced number of items administered –Selected items cover limited range of measurement continuum

Item Selection in CAT Optimized for efficiency and precision of measurement estimation. –e.g., maximizing Fisher’s information function Alternative procedures could be devised to balance efficiency/precision and obtaining responses over a wider range of the measurement continuum –e.g., Linacre’s (1995) Bayesian falsification procedure

Purpose of Study Examine the predictive efficiency and sensitivity of various person fit indices to detecting misfit in CAT –Predictive efficiency: how well can we predict the overall pattern of misfit based on item responses collected via CAT? What effect does different item selection methods have on our ability to detect person misfit in a CAT context?

HypothesesHypotheses 1.Predictive efficiency of CAT-derived person fit statistics will be enhanced by selecting items from a wider range of the measurement continuum. 2.Greater predictive efficiency will improve detection of atypical responding.

Data Source and Simulation Procedure Data were from 4,360 individuals presenting to substance abuse treatment upon intake Post-hoc CAT simulations were performed: –One parameter IRT (Rasch) dichotomous response model. –Maximum-likelihood estimation –Item Selection Procedures Modified “Bayesian” falsification procedure (MBF) Maximum Fisher’s Information (MFI) –Stop Rule: all items were administered to examine the effects of successive item administration on person fit indices.

Internal Mental Distress Scale The IMDS is a 42-item instrument that is part of the Global Appraisal of Individual Needs (Dennis et al., 2003). Measures: –Internal mental distress (second-order factor) –Depression –Anxiety –Trauma –Homicidality/Suicidality –Somatic complaints Validated using a 1-parameter IRT (Rasch) measurement model

Modified Bayesian Falsification Item Selection (MBF) 1.Set the start value for the measure (θ 0 ) at 0 logits. 2.Calculate a “target” measure: i.If previous item was endorsed or first item: θ T = θ i-1 + max(2,SE 2 ) ii.Otherwise: θ T = θ i-1 – max(2,SE 2 ) 3.For each unadministered item, compute the information function I ni (θ T ). 4.Select the item with the largest information function.

Person Fit Statistics Residual-based: –Infit, outfit (Wright & Stone, 1979; Wright, 1980) –Log infit and outfit (Wright & Stone, 1979) Non-Parametric –Modified Caution Index (MCI; Harnisch & Linn, 1981) –H T (Sijtsma, 1986; Sijtsma & Meier, 1992) Likelihood-Based –lz (Drasgow, Levine & Williams, 1985) CAT-Specific (CUSUM; van Krimpen-Stoop & Meijer, 2000) –Used three different methods for estimating response residuals (T1, T3, and T6).

Predictive Efficiency of Person Fit Statistics

Predictive Efficiency, MFI Item Selection

Predictive Efficiency, MBF Item Selection

Min. Number of Items to Achieve R 2 =.80 Fit StatisticMFIMBF MCI1311 HTHT 1817 Infit2019 Log Infit1516 Outfit3936 Log Outfit19 lZlZ 3834 CUSUM (T1)26 CUSUM (T3)3032 CUSUM (T6)3935 Average

Identification of Persons with Atypical Suicide

Atypical Suicide Conrad and colleagues (2010) identified a subgroup with suicidal ideation with lower levels of depression, anxiety, trauma In this study however, we defined atypical suicide as persons with: –2+ suicidal symptoms –Level of internal mental distress is not predictive of suicidality. –Under typical CAT operation, these individuals would be unlikely to receive suicide items during a CAT session

Suicide Groups Based on 2+ Symptoms N=7,348

Predicting Atypical Suicide: All Items VariableAUCSensitivitySpecificity IMDS MCI HTHT Infit/Log Infit Outfit Log Outfit lZlZ CUSUM (T1) CUSUM (T3) CUSUM (T6) Multivariate

Sensitivity to Predict Atypical Suicide

Comparison of Item Selection Procedures

First 5 Items Administered by CAT

CAT to Full Instrument Correlation

Measurement Precision (RMSE)

Test Information

A Case Example

MFI Item Selection and Measure Estimation First suicide item administered

MBF Item Selection and Measure Estimation First suicide item administered

ComparisonComparison MFIMBFFull Measure Std. Error Outfit Infit lzlz # Suicide035 # Administered192242

ConclusionsConclusions Hypothesis 1: Item selection method had only a modest effect on predictive efficiency, though in the hypothesized direction. –MBF had strongest effect on outfit, l z and CUSUM (T6) Partial support for Hypothesis 2: –MBF provided efficient detection of atypical suicide pattern –Reflects the type of items selected early in the CAT rather than on predictive efficiency MBF was found to be somewhat less efficient than MFI

Strengths and Limitations Strengths –Large sample –Clinical sample –Several fit statistics examined Limitations –Multidimensionality –Small item bank –Further work needed on defining “atypicalness” in clinical context –Further validation of approach across instruments, measurement models

ReferencesReferences Conrad, K. J., Bezruczko, N., Chan, Y. F., Riley, B., Diamond, G., & Dennis, M. L. (2010). Screening for atypical suicide risk with person fit statistics among people presenting to alcohol and other drug treatment. Drug and Alcohol Dependence, 106(1), Drasgow, F., Levine, M. V., & McLaughlin, M. E. (1987). Detecting inappropriate test scores with optimal and practical appropriateness indices. Applied Psychological Measurement, 11(1), Harnisch, D. L., & Linn, R. L. (1981). Analysis of item response patterns: Questionable test data and dissimilar curriculum practices. Journal of Educational Measurement, 18(2), Linacre, J. M. (1995). Computer-adaptive testing CAT: A Bayesiian approach. Rasch Measurement Transactions, 9(1), 412. Sijtsma, K. (1986). A coefficient of deviance of response patterns. Kwantitatieve Methoden, 7, 131–145. Sijtsma, K., & Meijer, R. R. (1992). A method for investigating the intersection of item response functions in Mokken’s non-parametric IRT model. Applied Psychological Measurement, 16(2), van Krimpen-Stoop, E. M., & Meijer, R. R. (2000). Detecting person misfit in adaptive testing using statistical process control techniques. In W.J. van der Linden and C.A.W. Glas (Ed.), Computer adaptive testing: Theory and practice. Boston: Kluwer Academic. Wright, B. D. (1980). Afterword. In G. Rasch (Ed.), Probabilistic models for some intelligence and attainment tests: With foreword and afterword by Benjamin D. Wright. Chicago: MESA Press. Wright, B. D., & Stone, M. H. (1979). Best test design. Chicago: University of Chicago, MESA Press.

Thank you! For more information, contact: Barth Riley, Ph.D. For more information about the psychometrics of the Global Appraisal of Individual Needs (GAIN), including the Internal Mental Distress Scale, go to: