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Social desirability bias (SDB) is a common threat to the validity of conclusions from responses to a scale or survey. There is a wide range of person-fit statistics in the literature that can be employed to detect SDB. In addition, machine learning classifiers, such as logistic regression and random forest, have the potential to distinguish between biased and unbiased responses. This study proposes a new application of these classifiers to detect SDB by considering several person-fit indices as features or predictors in the machine learning methods. The results of a Monte Carlo simulation study showed that for a single feature, applying person-fit indices directly and logistic regression led to similar classification results. However, the random forest classifier improved the classification of biased and unbiased responses substantially. Classification was improved in both logistic regression and random forest by considering multiple features simultaneously. Moreover, cross-validation indicated stable area under the curves (AUCs) across machine learning classifiers. A didactical illustration of applying random forest to detect SDB is presented. [ABSTRACT FROM AUTHOR]

Introduction: This study investigates the confounding effects of reverse-coded items on the measurement of confidence in mathematics using data from the 2019 Trends in International Mathematics and Science Study (TIMSS). Methods: The sample came from the Saudi Arabian cohort of 8th graders in 2019 involving 4,515 students. Through mixture modeling, two subgroups responding in similar ways to reverse-coded items were identified representing approximately 9% of the sample. Results: Their response to positively valenced and negatively valenced items showed inconsistency and the observed unexpected response patterns were further verified using Lz*, U3, and the number of Guttman errors person fit indicators. Psychometric analyses on the full sample and the truncated sample after deleting the aberrant responders indicated significant improvements in both internal consistency reliability and factorial validity. Discussion: It was concluded that reverse-coded items contribute to systematic measurement error that is associated with distorted item level parameters that compromised the scale's reliability and validity. The study underscores the need for reconsideration of reverse-coded items in survey design, particularly in contexts involving younger populations and low-achieving students. [ABSTRACT FROM AUTHOR]

IntroductionThis study investigates the confounding effects of reverse-coded items on the measurement of confidence in mathematics using data from the 2019 Trends in International Mathematics and Science Study (TIMSS).MethodsThe sample came from the Saudi Arabian cohort of 8th graders in 2019 involving 4,515 students. Through mixture modeling, two subgroups responding in similar ways to reverse-coded items were identified representing approximately 9% of the sample.ResultsTheir response to positively valenced and negatively valenced items showed inconsistency and the observed unexpected response patterns were further verified using Lz*, U3, and the number of Guttman errors person fit indicators. Psychometric analyses on the full sample and the truncated sample after deleting the aberrant responders indicated significant improvements in both internal consistency reliability and factorial validity.DiscussionIt was concluded that reverse-coded items contribute to systematic measurement error that is associated with distorted item level parameters that compromised the scale’s reliability and validity. The study underscores the need for reconsideration of reverse-coded items in survey design, particularly in contexts involving younger populations and low-achieving students.

Social desirability bias (SDB) has been a major concern in educational and psychological assessments when measuring latent variables because it has the potential to introduce measurement error and bias in assessments. Person-fit indices can detect bias in the form of misfitted response vectors. The objective of this study was to compare the performance of 14 person-fit indices to identify SDB in simulated responses. The area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis was computed to evaluate the predictive power of these statistics. The findings showed that the agreement statistic (A) outperformed all other person-fit indices, while the disagreement statistic (D) , dependability statistic (E) , and the number of Guttman errors (G) also demonstrated high AUCs to detect SDB. Recommendations for practitioners to use these fit indices are provided. [ABSTRACT FROM AUTHOR]

Careless responding by mental health patients on self-report assessments is rarely investigated in routine care despite the potential for serious consequences such as faulty clinical decisions. We investigated validity indices most appropriate for detecting careless responding in routine outcome monitoring (ROM) in mental health-care. First, we reviewed indices proposed in previous research for their suitability in ROM. Next, we evaluated six selected indices using data of the Brief Symptom Inventory and the Mood and Anxiety Symptom Questionnaire from 3,483 outpatients. Simulations showed that for typical ROM scales the index, Mahalanobis distance, and inter-item standard deviation may be too strongly confounded with the latent trait value to compare careless responding across patients with different symptom severity. Application of two different classification methods to the validity indices did not converge in similar prevalence estimates of careless responding. Finally, results suggest that careless responding does not have a substantial biasing effect on scale-score statistics. We recommend the person-fit index to screen for random careless responding in large ROM data sets. However, additional research should further investigate methods for detecting repetitive responding in typical ROM data and assess whether there are specific circumstances in which simpler validity statistics or direct screening methods perform similarly as the index. [ABSTRACT FROM AUTHOR]