The Effect of Rapport on Data Quality in Face-to-Face Interviews: Beneficial or Detrimental?

The benefits of rapport between interviewers and respondents, in terms of recruiting the latter and motiving them to participate in research, have been generally endorsed. However, there has been less clarity with regard to the association between rapport and data quality. In theory, rapport could be beneficial if it motivates people to give complete and honest responses. On the other hand, efforts to maintain rapport by exhibiting pleasing and socially desirable behaviour could well be detrimental to data quality. In a large longitudinal epidemiological sample, generalized estimating equations (GEE) analyses were used to examine the association between rapport and the following three quality indicators: missing responses, responses to sensitive questions, and consistency of responses. The results of these analyses indicate an association between a high level of rapport and fewer missing responses. In contrast, we found more socially desirable responses for the high-rapport group. Finally, the high-rapport group did not differ from the low-rapport group in terms of the consistency of their responses.

A hierarchical latent response model for inferences about examinee engagement in terms of guessing and item-level non-response

In low-stakes assessments, test performance has few or no consequences for examinees themselves, so that examinees may not be fully engaged when answering the items. Instead of engaging in solution behaviour, disengaged examinees might randomly guess or generate no response at all. When ignored, examinee disengagement poses a severe threat to the validity of results obtained from low-stakes assessments. Statistical modelling approaches in educational measurement have been proposed that account for non-response or for guessing, but do not consider both types of disengaged behaviour simultaneously. We bring together research on modelling examinee engagement and research on missing values and present a hierarchical latent response model for identifying and modelling the processes associated with examinee disengagement jointly with the processes associated with engaged responses. To that end, we employ a mixture model that identifies disengagement at the item-by-examinee level by assuming different data-generating processes underlying item responses and omissions, respectively, as well as response times associated with engaged and disengaged behaviour. By modelling examinee engagement with a latent response framework, the model allows assessing how examinee engagement relates to ability and speed as well as to identify items that are likely to evoke disengaged test-taking behaviour. An illustration of the model by means of an application to real data is presented.

Using Response Times for Joint Modeling of Response and Omission Behavior

For adequate modeling of missing responses, a thorough understanding of the nonresponse mechanisms is vital. As a large number of major testing programs are in the process or already have been moving to computer-based assessment, a rich body of additional data on examinee behavior becomes easily accessible. These additional data may contain valuable information on the processes associated with nonresponse. Bringing together research on item omissions with approaches for modeling response time data, we propose a framework for simultaneously modeling response behavior and omission behavior utilizing timing information for both. As such, the proposed model allows (a) to gain a deeper understanding of response and nonresponse behavior in general and, in particular, of the processes underlying item omissions in LSAs, (b) to model the processes determining the time examinees require to generate a response or to omit an item, and (c) to account for nonignorable item omissions. Parameter recovery of the proposed model is studied within a simulation study. An illustration of the model by means of an application to real data is provided.

Using Response Times to Model Not-Reached Items due to Time Limits

Missing values at the end of a test typically are the result of test takers running out of time and can as such be understood by studying test takers' working speed. As testing moves to computer-based assessment, response times become available allowing to simulatenously model speed and ability. Integrating research on response time modeling with research on modeling missing responses, we propose using response times to model missing values due to time limits. We identify similarities between approaches used to account for not-reached items (Rose et al. in ETS Res Rep Ser 2010:i-53, 2010) and the speed-accuracy (SA) model for joint modeling of effective speed and effective ability as proposed by van der Linden (Psychometrika 72(3):287-308, 2007). In a simulation, we show (a) that the SA model can recover parameters in the presence of missing values due to time limits and (b) that the response time model, using item-level timing information rather than a count of not-reached items, results in person parameter estimates that differ from missing data IRT models applied to not-reached items. We propose using the SA model to model the missing data process and to use both, ability and speed, to describe the performance of test takers. We illustrate the application of the model in an empirical analysis.

Investigation of Missing Responses in Q-Matrix Validation

Missing data can be a serious issue for practitioners and researchers who are tasked with Q-matrix validation analysis in implementation of cognitive diagnostic models. The article investigates the impact of missing responses, and four common approaches (treat as incorrect, logistic regression, listwise deletion, and expectation-maximization [EM] imputation) for dealing with them, on the performance of two major Q-matrix validation methods (the EM-based δ-method and the nonparametric Q-matrix refinement method) across multiple factors. Results of the simulation study show that both validation methods perform better when missing responses are imputed using EM imputation or logistic regression instead of being treated as incorrect and using listwise deletion. The nonparametric Q-matrix validation method outperforms the EM-based δ-method in most conditions. Higher missing rates yield poorer performance of both methods. Number of attributes and items have an impact on performance of both methods as well. Results of a real data example are also discussed in the study.

Testing Students with Special Educational Needs in Large-Scale Assessments - Psychometric Properties of Test Scores and Associations with Test Taking Behavior

Assessing competencies of students with special educational needs in learning (SEN-L) poses a challenge for large-scale assessments (LSAs). For students with SEN-L, the available competence tests may fail to yield test scores of high psychometric quality, which are—at the same time—measurement invariant to test scores of general education students. We investigated whether we can identify a subgroup of students with SEN-L, for which measurement invariant competence measures of adequate psychometric quality may be obtained with tests available in LSAs. We furthermore investigated whether differences in test-taking behavior may explain dissatisfying psychometric properties and measurement non-invariance of test scores within LSAs. We relied on person fit indices and mixture distribution models to identify students with SEN-L for whom test scores with satisfactory psychometric properties and measurement invariance may be obtained. We also captured differences in test-taking behavior related to guessing and missing responses. As a result we identified a subgroup of students with SEN-L for whom competence scores of adequate psychometric quality that are measurement invariant to those of general education students were obtained. Concerning test taking behavior, there was a small number of students who unsystematically picked response options. Removing these students from the sample slightly improved item fit. Furthermore, two different patterns of missing responses were identified that explain to some extent problems in the assessments of students with SEN-L.