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Progress feedback is an intervention aimed at enhancing patient outcomes in routine clinical practice. This study reports a comprehensive multilevel meta-analysis on the effectiveness of progress feedback in psychological treatments in curative care. The short- and long-term effects of feedback on symptom reduction were investigated using 58 (randomized and non-randomized) studies, analyzing 110 effect sizes in a total of 21,699 patients. Effects of feedback on dropout rate, percentage of deteriorated cases, and treatment duration were also examined. Moderation analyses were conducted for study and feedback characteristics. A small significant effect of progress feedback on symptom reduction (d = 0.15, 95% CI: [0.10, 0.20]) was found, compared to control groups. This was also true for not-on-track cases (d = 0.17, 95% CI: [0.11, 0.22]). In addition, feedback had a small favorable effect on dropout rates (OR = 1.19, 95% CI: [1.03, 1.38]). The moderation analyses identified several potentially interesting variables for further research, including feedback instrument, outcome instrument, type of feedback, feedback frequency, treatment intensity, and country in which the study was conducted. Future studies should report on these variables more consistently so that we can obtain a better understanding of when and why feedback improves outcomes., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In psychological assessment of children, it is pivotal to establish from what age on self-reports can complement or replace informant reports. We introduce a psychometric approach to estimate the minimum age for a child to produce self-report data that is of similar quality as informant data. The approach makes use of statistical validity indicators such as person-fit and long-string indices, and can be readily applied to data commonly collected in psychometric studies of child measures. We evaluate and illustrate the approach, using self-report and informant-report data of the PedsQL, a pediatric health-related quality of life measure, from 651 child-mother pairs. To evaluate the approach, we tested various hypotheses about the validity of the self-report data, using the G n p person-fit index as the validity indicator and the mother informant-data as a benchmark for validity. Results showed that G n p discriminated between self-reports of younger and older children, between self-reports of children that completed the PedsQL alone or with a parent, and between self-reports and informant reports. We conclude that the validity-index approach has good potential for future applications. Future research should further evaluate the approach for different types of questionnaires (e.g., personality inventories) and using different validity indices (e.g., response-bias indices).

Respondents may use satisficing (i.e., nonoptimal) strategies when responding to self-report questionnaires. These satisficing strategies become more likely with decreasing motivation and/or cognitive ability (Krosnick, 1991). Considering that cognitive deficits are characteristic of depressive and anxiety disorders, depressed and anxious patients may be prone to satisficing. Using data from the Netherland's Study of Depression and Anxiety ( N = 2,945), we studied the relationship between depression and anxiety, cognitive symptoms, and satisficing strategies on the NEO Five-Factor Inventory. Results showed that respondents with either an anxiety disorder or a comorbid anxiety and depression disorder used satisficing strategies substantially more often than healthy respondents. Cognitive symptom severity partly mediated the effect of anxiety disorder and comorbid anxiety disorder on satisficing. The results suggest that depressed and anxious patients produce relatively low-quality self-report data-partly due to cognitive symptoms. Future research should investigate the degree of satisficing across different mental health care assessment contexts.

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 L m a x 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 l z p 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 l z p index.

The aim of this study was to assess the extent to which discrepancy between self-reported and clinician-rated severity of depression are due to inconsistent self-reports. Response inconsistency threatens the validity of the test score. We used data from a large sample of outpatients ( N = 5,959) who completed the self-report Beck Depression Inventory-II (BDI-II) and the clinician-rated Montgomery-Åsberg Depression Rating Scale (MADRS). We used item response theory based person-fit analysis to quantify the inconsistency of the self-report item scores. Inconsistency was weakly positively related to patient-clinician discrepancy (i.e., higher BDI-II scores relative to MADRS scores). The mediating effect of response inconsistency in the relationship between discrepancy and demographic (e.g., ethnic origin) and clinical variables (e.g., cognitive problems) was negligible. The small direct and mediating effects of response inconsistency suggest that inaccurate patient self-reports are not a major cause of patient-clinician discrepancy in outpatient samples. Future research should investigate the role of clinician biases in explaining clinician-patient discrepancy.

Background: Two important goals when using questionnaires are (a) measurement: the questionnaire is constructed to assign numerical values that accurately represent the test taker's attribute, and (b) prediction: the questionnaire is constructed to give an accurate forecast of an external criterion. Construction methods aimed at measurement prescribe that items should be reliable. In practice, this leads to questionnaires with high inter-item correlations. By contrast, construction methods aimed at prediction typically prescribe that items have a high correlation with the criterion and low inter-item correlations. The latter approach has often been said to produce a paradox concerning the relation between reliability and validity [1-3], because it is often assumed that good measurement is a prerequisite of good prediction., Objective: To answer four questions: (1) Why are measurement-based methods suboptimal for questionnaires that are used for prediction? (2) How should one construct a questionnaire that is used for prediction? (3) Do questionnaire-construction methods that optimize measurement and prediction lead to the selection of different items in the questionnaire? (4) Is it possible to construct a questionnaire that can be used for both measurement and prediction?, Illustrative Example: An empirical data set consisting of scores of 242 respondents on questionnaire items measuring mental health is used to select items by means of two methods: a method that optimizes the predictive value of the scale (i.e., forecast a clinical diagnosis), and a method that optimizes the reliability of the scale. We show that for the two scales different sets of items are selected and that a scale constructed to meet the one goal does not show optimal performance with reference to the other goal., Discussion: The answers are as follows: (1) Because measurement-based methods tend to maximize inter-item correlations by which predictive validity reduces. (2) Through selecting items that correlate highly with the criterion and lowly with the remaining items. (3) Yes, these methods may lead to different item selections. (4) For a single questionnaire: Yes, but it is problematic because reliability cannot be estimated accurately. For a test battery: Yes, but it is very costly. Implications for the construction of patient-reported outcome questionnaires are discussed.

Person misfit on a self-report measure refers to a response pattern that is unlikely given a theoretical measurement model. Person misfit may reflect low quality self-report data, for example due to random responding or misunderstanding of items. However, recent research in the context of psychopathology suggests that person misfit may reflect atypical symptom profiles that have implications for diagnosis or treatment. We followed-up on Wanders et al. (Journal of Affective Disorders, 180, 36-43, 2015) who investigated person misfit on the Inventory of Depressive Symptomatology (IDS) in the Netherlands Study of Depression and Anxiety (n = 2,981). Our goal was to investigate the extent to which misfit on the IDS reflects low-quality self-report patterns and the extent to which it reflects true atypical symptom profiles. Regression analysis showed that person misfit related more strongly to self-report quality indicators than to variables quantifying theoretically-derived atypical symptom profiles. A data-driven atypical symptom profile explained most variance in person misfit, suggesting that person misfit on the IDS mainly reflects a sample- and questionnaire-specific atypical symptom profile. We concluded that person-fit statistics are useful for detecting IDS scores that may not be valid. Further research is necessary to support the interpretation of person misfit as reflecting a meaningful atypical symptom combination., (Copyright © 2016 John Wiley & Sons, Ltd.)

The Leiden Index of Depression Sensitivity (LEIDS; Van der Does, 2002a) is a self-report measure of cognitive reactivity (CR) to sad mood. The LEIDS and its revised version, LEIDS-R (Van der Does & Williams, 2003), reliably distinguish between depression-vulnerable and healthy populations. They also correlate with other markers of depression vulnerability, but little is known about the other psychometric properties. Our aim was to examine the factor structure and validity of the LEIDS-R. We used data from the Netherlands Study of Depression and Anxiety (NESDA; N = 1,696) and a student sample (N = 811) for exploratory and confirmatory factor analysis (EFA and CFA, respectively). CFA showed that model fit of the 6-factor structure was satisfactory in the NESDA sample, but some factors were highly correlated. After removing 4 poor items, EFA yielded an alternative 5-factor structure and could not replicate the original 6-factor model. Testing for measurement invariance across recruitment groups of NESDA showed support for strong invariance. Due to high interfactor correlations, a bifactor model with 1 general factor and 5 specific factors was fitted in 2 samples. This model supported use of a general factor, but high factor loadings in specific factors supported retaining a 5-subscale structure. Higher scores on the general factor were associated with a history of depression, especially in participants with a history of comorbid anxiety. We concluded that the LEIDS-R has good psychometric properties. A modified version, LEIDS-RR, comprised of 5 subscales and a total CR score, is recommended for future research. One of the subscales is suitable as a short form. (PsycINFO Database Record, ((c) 2017 APA, all rights reserved).)

Latent class (LC) cluster analysis of a set of subscale l z person-fit statistics was proposed to explain person misfit on multiscale measures. The proposed explanatory LC person-fit analysis was used to analyze data of students ( N = 91,648) on the nine-subscale School Attitude Questionnaire Internet (SAQI). Inspection of the class-specific l z mean and variance structure combined with explanatory analysis of class membership showed that the data included a poor-fit class, a class showing good fit combined with social desirability bias, a good-fit class, and two classes that were more difficult to interpret. A comparison of multinomial logistic regression predicting class membership and multiple regression predicting continuous person fit showed that LC cluster analysis provided information about aberrant responding unattainable by means of linear multiple regression. It was concluded that LC person-fit analysis has added value to common approaches to explaining aberrant responding to multiscale measures., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

We applied item response theory based person-fit analysis (PFA) to data of the Outcome Questionnaire-45 (OQ-45) to investigate the prevalence and causes of aberrant responding in a sample of Dutch clinical outpatients. The [Formula: see text] person-fit statistic was used to detect misfitting item-score patterns and the standardized residual statistic for identifying the source of the misfit in the item-score patterns identified as misfitting. Logistic regression analysis was used to predict person misfit from clinical diagnosis, OQ-45 total score, and Global Assessment of Functioning code. The [Formula: see text] statistic classified 12.6% of the item-score patterns as misfitting. Person misfit was positively related to the severity of psychological distress. Furthermore, patients with psychotic disorders, somatoform disorders, or substance-related disorders more likely showed misfit than the baseline group of patients with mood and anxiety disorders. The results suggest that general outcome measures such as the OQ-45 are not equally appropriate for patients with different disorders. Our study emphasizes the importance of person-misfit detection in clinical practice., (© The Author(s) 2014.)

Self-report measures are vulnerable to concentration and motivation problems, leading to responses that may be inconsistent with the respondent's latent trait value. We investigated response consistency in a sample (N = 860) of cardiac patients with an implantable cardioverter defibrillator and their partners who completed the Spielberger State-Trait Anxiety Inventory on five measurement occasions. For each occasion and for both the state and trait subscales, we used the l (p) z person-fit statistic to assess response consistency. We used multilevel analysis to model the between-person and within-person differences in the repeated observations of response consistency using time-dependent (e.g., mood states) and time-invariant explanatory variables (e.g., demographic characteristics). Respondents with lower education, undergoing psychological treatment, and with more post-traumatic stress disorder symptoms tended to respond less consistently. The percentages of explained variance in response consistency were small. Hence, we conclude that the results give insight into the causes of response inconsistency but that the identified explanatory variables are of limited practical value for identifying respondents at risk of producing invalid test results. We discuss explanations for the small percentage of explained variance and suggest alternative methods for studying causes of response inconsistency.

The logistic person response function (PRF) models the probability of a correct response as a function of the item locations. Reise (2000) proposed to use the slope parameter of the logistic PRF as a person-fit measure. He reformulated the logistic PRF model as a multilevel logistic regression model and estimated the PRF parameters from this multilevel framework. An advantage of the multilevel framework is that it allows relating person fit to explanatory variables for person misfit/fit. We critically discuss Reise's approach. First, we argue that often the interpretation of the PRF slope as an indicator of person misfit is incorrect. Second, we show that the multilevel logistic regression model and the logistic PRF model are incompatible, resulting in a multilevel person-fit framework, which grossly violates the bivariate normality assumption for residuals in the multilevel model. Third, we use a Monte Carlo study to show that in the multilevel logistic regression framework estimates of distribution parameters of PRF intercepts and slopes are biased. Finally, we discuss the implications of these results and suggest an alternative multilevel regression approach to explanatory person-fit analysis. We illustrate the alternative approach using empirical data on repeated anxiety measurements of cardiac arrhythmia patients who had a cardioverter-defibrillator implanted.