Applied Psychological Measurement最新文献

ierfair: An R Package for Ability-Conditioned Fairness Diagnostics of Insufficient-Effort Responding on Background Questionnaires. 一个能力条件公平性诊断的R包——努力不足的背景问卷回答。

IF 1.6 4区心理学

Applied Psychological Measurement Pub Date : 2026-10-10 DOI: 10.1177/01466216261498417

Diana Rosita, Muhammad Sukirlan, Nurlaksana Eko Rusminto, Budi Kadaryanto

{"title":"ierfair: An R Package for Ability-Conditioned Fairness Diagnostics of Insufficient-Effort Responding on Background Questionnaires.","authors":"Diana Rosita, Muhammad Sukirlan, Nurlaksana Eko Rusminto, Budi Kadaryanto","doi":"10.1177/01466216261498417","DOIUrl":"https://doi.org/10.1177/01466216261498417","url":null,"abstract":"Insufficient-effort/careless responding (IER) on self-report background questionnaires attached to large-scale assessments (e.g., PIRLS, PISA, and TIMSS) is routinely screened with indicator-based methods (e.g., longstring, response variability, Mahalanobis distance, and psychometric synonym/antonym consistency). These indices are computed from a respondent's own response pattern and implicitly treat any inconsistency as inattention. We show this assumption is not fairness-neutral: lower-ability respondents may produce more inconsistent-looking patterns simply because background-questionnaire items are harder for them to process, independent of genuine effort. We introduce ierfair, an R package that benchmarks standard IER indices against ability-stratified (or local-regression) reference bands built from an external achievement score, flagging a respondent only when their index is extreme relative to others at the same ability level. Applying ierfair to the PIRLS 2011 Indonesia Student Background Questionnaire (N = 4,720), we confirm a significant ability confound in three raw indices (r = -.15 to .25) that per-index conditioning removes but find that a natural rule for combining several conditioned indices (\"flag if ≥ k of m fire\") can reintroduce a significant residual ability trend even when no individual index shows one. Requiring unanimity or switching to local-regression conditioning reduces this trend, and pooling indices into a single score before thresholding, implemented as iec_composite (), removes it most cleanly, keeping the flag rate within 1-2 percentage points of the sample mean across every stratum. The package requires no response-time data, an important constraint for archival large-scale-assessment data, and produces an audit-trail report summarizing flag rates across ability strata and demographic subgroups.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":" ","pages":"01466216261498417"},"PeriodicalIF":1.6,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13654004/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149171298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

grmcor: An R Package for Range-Restriction Bias Correction of Discrimination Parameters in the Graded Response Model. grmcor：分级响应模型中判别参数距离限制偏置校正的R包。

IF 1.6 4区心理学

Applied Psychological Measurement Pub Date : 2026-10-07 DOI: 10.1177/01466216261492527

Hikmayanti Arifuddin, Abdul Saman, Mansyur, Herman, Indriani H Ismail, Zahratul Qalbi, Bayu Fitra Prisuna, Abdul Malik

{"title":"grmcor: An R Package for Range-Restriction Bias Correction of Discrimination Parameters in the Graded Response Model.","authors":"Hikmayanti Arifuddin, Abdul Saman, Mansyur, Herman, Indriani H Ismail, Zahratul Qalbi, Bayu Fitra Prisuna, Abdul Malik","doi":"10.1177/01466216261492527","DOIUrl":"10.1177/01466216261492527","url":null,"abstract":"The Graded Response Model (GRM) is widely used for analyzing polytomous response data from attitude, personality, and character measurement instruments. However, when response data are collected from a range-restricted sample such as selected applicants, screened participants, or high-performing students, GRM discrimination parameters are systematically underestimated. No existing R package implements range-restriction bias correction specifically for GRM discrimination parameters. This paper introduces grmcor, an R package that implements the truncated normal standard deviation ratio correction for GRM discrimination parameters under direct range restriction. The package provides functions for estimating GRM parameters from restricted samples, applying the correction, comparing restricted and corrected estimates, visualizing bias patterns, and validating the correction procedure via Monte Carlo simulation. Monte Carlo results demonstrate that grmcor substantially reduces bias in discrimination parameter estimates, with mean bias reduced from -0.64 to near zero and RMSE reduced by approximately 52% under 50% range restriction. The package is freely available via GitHub.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":" ","pages":"01466216261492527"},"PeriodicalIF":1.6,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13646184/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149138903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Selecting Between Whole-Sample and Split-Sample Strategies for Exploratory and Confirmatory Factor Analyses: Guidance for Psychometric Scale Development. 探索性和验证性因素分析的全样本和分样本策略选择：心理测量量表开发指南。

IF 1.6 4区心理学

Applied Psychological Measurement Pub Date : 2026-09-30 DOI: 10.1177/01466216261492558

Arash Arianpoor, Craig S Webster, Silas C R Taylor, Boaz Shulruf, Pin-Hsiang Huang

{"title":"Selecting Between Whole-Sample and Split-Sample Strategies for Exploratory and Confirmatory Factor Analyses: Guidance for Psychometric Scale Development.","authors":"Arash Arianpoor, Craig S Webster, Silas C R Taylor, Boaz Shulruf, Pin-Hsiang Huang","doi":"10.1177/01466216261492558","DOIUrl":"https://doi.org/10.1177/01466216261492558","url":null,"abstract":"Development of robust psychometric scales demands careful consideration of both sample size and analytic strategy. This simulation compared whole-sample, bootstrap, and split-sample approaches for exploratory and confirmatory factor analysis (EFA/CFA) across eight realistic sample sizes. Rather than asking whether small samples are problematic, a question that is already well established, we examined when splitting an available sample compromises factor-pattern recovery and CFA stability. Using synthetic data and resampling, we assessed effects on model fit, reliability, and factor structure reproducibility. Results showed that split-sample validation became most defensible when each half independently met minimum adequacy conditions; otherwise, whole-sample analysis supplemented by resampling-based stability checks provided more consistent evidence. Based on these findings, we propose a pragmatic decision framework for applied scale development. As lower-bound guidance, our results support a minimum of 200 cases for EFA and CFA and a case-to-item ratio of at least 10:1 to ensure replicability. When these conditions are not met, whole-sample or bootstrap-based approaches are likely to provide more stable solutions. Researchers should also consider model- and design-specific requirements, which may necessitate substantially larger samples than these minimum recommendations. In conclusion, these empirically grounded recommendations provide researchers with actionable guidance for optimising sample size and analytic approach, enhancing the validity and generalisability of psychometric instruments in psychoeducational measurement.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":" ","pages":"01466216261492558"},"PeriodicalIF":1.6,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13627465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying Item Preknowledge Through Convergence Iteration Count Analysis in Ability Estimation. 能力估计中的收敛迭代计数分析识别项目预知。

IF 1.6 4区心理学

Applied Psychological Measurement Pub Date : 2026-09-30 DOI: 10.1177/01466216261489271

Han Han, Ruihang He, Yongze Xu

{"title":"Identifying Item Preknowledge Through Convergence Iteration Count Analysis in Ability Estimation.","authors":"Han Han, Ruihang He, Yongze Xu","doi":"10.1177/01466216261489271","DOIUrl":"https://doi.org/10.1177/01466216261489271","url":null,"abstract":"Item preknowledge (IP) is a severe form of cheating that fundamentally undermines the fairness and score interpretability of educational assessments. Existing detection methods for IP are predominantly constructed under the item response theory (IRT) framework, but these conventional indicators are inherently static: they rely solely on the final converged estimates of item parameters and examinee ability, while completely overlooking the rich dynamic information embedded in the iterative convergence process of ability estimation. To address this core limitation, this paper proposes a novel dynamic detection indicator-the convergence iteration count (CIC)-defined as the number of iterations required for the IRT model to converge to a stable ability estimate for each examinee. We validate the effectiveness of the CIC indicator through Monte Carlo simulated experiments across diverse scenarios, with varying proportions of compromised items and levels of misclassification uncertainty of compromised items, covering both known and unknown compromised item range conditions. Results show that the CIC indicator significantly outperforms traditional static indicators (including the TW, KL, and SR indicators) in identifying IP examinees. Notably, even in the extreme complex scenario with a 60% high proportion of compromised items and 50% high misclassification uncertainty, the area under the curve of the CIC indicator remains approaching or exceeding 0.8. The CIC indicator also exhibits strong robustness in scenarios where the range of compromised items is unknown, with detection performance surpassing most conventional non-prior-information based indicators.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":" ","pages":"01466216261489271"},"PeriodicalIF":1.6,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13627467/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparative Study on the Detection Efficacy of CUSUM and CPA in Multidimensional Tests. CUSUM与CPA在多维测试中检测效果的比较研究。

IF 1.6 4区心理学

Applied Psychological Measurement Pub Date : 2026-09-30 DOI: 10.1177/01466216261488861

Xinyu Liu, Yinhong He, Yuxing Zhang, Hengxin Pang

{"title":"A Comparative Study on the Detection Efficacy of CUSUM and CPA in Multidimensional Tests.","authors":"Xinyu Liu, Yinhong He, Yuxing Zhang, Hengxin Pang","doi":"10.1177/01466216261488861","DOIUrl":"https://doi.org/10.1177/01466216261488861","url":null,"abstract":"Cumulative summation (CUSUM) and change point analysis (CPA) procedures are the two most widely used methods for flagging aberrant item responses, yet their relative merits have been examined almost exclusively in unidimensional settings. This study evaluated five person-fit statistics (PFSs) that have demonstrated strong detection power in earlier research: the CUSUM-based ( <math> <mrow><msubsup><mi>C</mi> <mi>i</mi> <mi>T</mi></msubsup> <mo>,</mo> <msubsup><mi>C</mi> <mi>i</mi> <mrow><mi>L</mi> <mi>A</mi> <mi>R</mi> <mi>D</mi></mrow> </msubsup> </mrow> </math> ), and the CPA-based ( <math> <mrow><msub><mi>L</mi> <mi>max</mi></msub> <mo>,</mo> <msub><mi>W</mi> <mi>max</mi></msub> <mo>,</mo> <msub><mi>R</mi> <mi>max</mi></msub> </mrow> </math> ), in multidimensional testing scenarios. In addition, the Wald statistic was derived to a new form for multidimensional tests, which is much different from the unidimensional version. Both a simulation experiment and a case study were conducted to assess the performance of these PFSs in detecting back random response (BRR). Results showed that inter-dimensional ability correlation influenced detection rates, and this effect diminished as test length increased. Across all conditions, the newly proposed <math> <mrow><msub><mi>W</mi> <mi>max</mi></msub> </mrow> </math> statistic achieved higher average detection efficiency than its competitors and exhibited the greatest stability.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":" ","pages":"01466216261488861"},"PeriodicalIF":1.6,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13627468/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

cdcatR: An R Package for Developing and Evaluating Cognitive Diagnosis Computerized Adaptive Tests. 开发和评估认知诊断计算机自适应测试的R包。

IF 1.6 4区心理学

Applied Psychological Measurement Pub Date : 2026-09-28 DOI: 10.1177/01466216261493974

Miguel A Sorrel, Pablo Nájera, Francisco J Abad

引用次数: 0

copyfair: An R Package for Item and Ability Conditioned Answer-Copying Detection in Multiple-Choice Examinations. 多项选择题中项目和能力条件下的抄答案检测的R包。

IF 1.6 4区心理学

Applied Psychological Measurement Pub Date : 2026-09-16 DOI: 10.1177/01466216261488632

Hera Hastuti, Tri Zahra Ningsih, Loso Judijanto, Ahmad Syaf'i, Sarah Samben, Suci Kurnia Putri

{"title":"copyfair: An R Package for Item and Ability Conditioned Answer-Copying Detection in Multiple-Choice Examinations.","authors":"Hera Hastuti, Tri Zahra Ningsih, Loso Judijanto, Ahmad Syaf'i, Sarah Samben, Suci Kurnia Putri","doi":"10.1177/01466216261488632","DOIUrl":"10.1177/01466216261488632","url":null,"abstract":"Answer-copying indices for multiple-choice examinations (e.g., the K-index, S2, ω, and the generalized binomial test) typically compare an examinee pair's error similarity against a single global or ability-marginal baseline-an approach that can conflate genuine collusion with population-wide \"attractive distractor\" effects, in which unrelated examinees independently select the same wrong option. We introduce copyfair, an R package that extends the ability-conditioned diagnostic logic of condfair to answer-copying detection: it models the expected number of matching incorrect responses jointly as a function of examinee ability and item-specific distractor popularity, tests localized disparity with a wild-bootstrap omnibus procedure, and returns Benjamini-Hochberg-corrected pairwise flags. A simulation study confirms the method recovers an injected, ability-concentrated collusion signal (p = .0005). Applied to the PIRLS 2011 Indonesia reading assessment (N = 4,788), an ability-only baseline over-flagged cross-school pairs sharing a common misconception (85.7% of flagged pairs), while the item-and-ability-conditioned model concentrated flags on same-classroom pairs (57.1%, a 114-fold enrichment over the 0.5% baseline rate), consistent with the physical/social proximity expected of genuine collusion.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":" ","pages":"01466216261488632"},"PeriodicalIF":1.6,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13582653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148991974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

csemTools: R Package for Estimating Conditional Standard Errors of Measurement for Test and Scale Scores. 用于估计测试和量表分数的条件标准误差的R包。

IF 1.6 4区心理学

Applied Psychological Measurement Pub Date : 2026-09-16 DOI: 10.1177/01466216261490085

César Merino-Soto

{"title":"csemTools: R Package for Estimating Conditional Standard Errors of Measurement for Test and Scale Scores.","authors":"César Merino-Soto","doi":"10.1177/01466216261490085","DOIUrl":"10.1177/01466216261490085","url":null,"abstract":"The R package csemTools provides a comprehensive implementation of classical procedures for estimating the conditional standard error of measurement (CSEM), a metric that has been underutilized in the validation of cognitive and non-cognitive scales based on dichotomous or ordinal responses. To support a robust and integrated workflow, csemTools offers a suite of functions that go beyond simple CSEM estimation. The package includes implementations of well-established methods from Feldt, Keats, Lord, Mollenkopf, and Thorndike. Building on the CSEM, it computes approximate conditional reliability via standardization, smooths CSEM curves to reduce local fluctuations, derives CSEMs for score groups defined by quantiles, and facilitates visual and quantitative comparisons across different CSEM procedures. Additionally, it provides an approximation for scale-score CSEM and a formal comparison of the global standard error of measurement against the entire CSEM curve. These features streamline the work of test developers and researchers, especially when reporting accuracy in non-educational or non-cognitive assessments. Ultimately, csemTools enables applied psychometricians to answer key questions about score precision: What is the CSEM for each score level? How do CSEM curves compare across methods? Is the global SEM representative of local precision? And what is the CSEM on the scale-score metric? By integrating these functionalities, the package fills a notable gap in the R ecosystem and promotes more nuanced and defensible interpretations of test scores.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":" ","pages":"01466216261490085"},"PeriodicalIF":1.6,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13582635/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ICC-Based Projection of IRT/MIRT Response Functions Into DINA-Style Diagnostic Summaries. 基于icc的IRT/MIRT反应函数投射到dina型诊断摘要。

IF 1.6 4区心理学

Applied Psychological Measurement Pub Date : 2026-09-09 DOI: 10.1177/01466216261487591

Tien-Yu Hsieh

引用次数: 0

facomplex: R Package for Analysis of Complexity and Simplicity in Factor Analysis. 因子分析中的复杂性和简单性分析的R包。

IF 1.6 4区心理学

Applied Psychological Measurement Pub Date : 2026-09-03 DOI: 10.1177/01466216261485859

César Merino-Soto, Sergio Domínguez-Lara

引用次数: 0