Xiaojing Kong

Bio: Xiaojing Kong is an academic researcher. The author has contributed to research in topics: Item response theory & Test (assessment). The author has an hindex of 1, co-authored 1 publications receiving 351 citations.

Response Time Effort: A New Measure of Examinee Motivation in Computer-Based Tests

Abstract: When low-stakes assessments are administered, the degree to which examinees give their best effort is often unclear, complicating the validity and interpretation of the resulting test scores. This study introduces a new method, based on item response time, for measuring examinee test-taking effort on computer-based test items. This measure, termed response time effort (RTE), is based on the hypothesis that when administered an item, unmotivated examinees will answer too quickly (i.e., before they have time to read and fully consider the item). Psychometric characteristics of RTE scores were empirically investigated and supportive evidence for score reliability and validity was found. Potential applications of RTE scores and their implications are discussed.

Detecting and Deterring Insufficient Effort Responding to Surveys

Abstract: Responses provided by unmotivated survey participants in a careless, haphazard, or random fashion can threaten the quality of data in psychological and organizational research. The purpose of this study was to summarize existing approaches to detect insufficient effort responding (IER) to low-stakes surveys and to comprehensively evaluate these approaches. In an experiment (Study 1) and a nonexperimental survey (Study 2), 725 undergraduates responded to a personality survey online. Study 1 examined the presentation of warnings to respondents as a means of deterrence and showed the relative effectiveness of four indices for detecting IE responses: response time, long string, psychometric antonyms, and individual reliability coefficients. Study 2 demonstrated that the detection indices measured the same underlying construct and showed the improvement of psychometric properties (item interrelatedness, facet dimensionality, and factor structure) after removing IE respondents identified by each index. Three approaches (response time, psychometric antonyms, and individual reliability) with high specificity and moderate sensitivity were recommended as candidates for future application in survey research. The identification of effective IER indices may help researchers ensure the quality of their low-stake survey data. This study is a first attempt to comprehensively evaluate IER detection methods using both experimental and nonexperimental designs. Results from both studies corroborated each other in suggesting the three more effective approaches. This study also provided convergent validity evidence regarding various indices for IER.

Separating the Shirkers from the Workers? Making Sure Respondents Pay Attention on Self‐Administered Surveys

Abstract: Good survey and experimental research requires subjects to pay attention to questions and treatments, but many subjects do not. In this article, we discuss “Screeners” as a potential solution to this problem. We first demonstrate Screeners’ power to reveal inattentive respondents and reduce noise. We then examine important but understudied questions about Screeners. We show that using a single Screener is not the most effective way to improve data quality. Instead, we recommend using multiple items to measure attention. We also show that Screener passage correlates with politically relevant characteristics, which limits the generalizability of studies that exclude failers. We conclude that attention is best measured using multiple Screener questions and that studies using Screeners can balance the goals of internal and external validity by presenting results conditional on different levels of attention.

Measuring Learning Outcomes in Higher Education: Motivation Matters

Abstract: With the pressing need for accountability in higher education, standardized outcomes assessments have been widely used to evaluate learning and inform policy. However, the critical question on how scores are influenced by students’ motivation has been insufficiently addressed. Using random assignment, we administered a multiple-choice test and an essay across three motivational conditions. Students’ self-report motivation was also collected. Motivation significantly predicted test scores. A substantial performance gap emerged between students in different motivational conditions (effect size as large as .68). Depending on the test format and condition, conclusions about college learning gain (i.e., value added) varied dramatically from substantial gain (d = 0.72) to negative gain (d = −0.23). The findings have significant implications for higher education stakeholders at many levels.