This study explores age-related differences in conformity to masculine norms between two distinct groups—emerging adults (26 and younger) and older adults (over 26)—using the Conformity to Masculine Norms Inventory (CMNI-29). The total sample consisted of 837 male participants (100%), aged between 18 and 64 years. Group distributions were as follows: emerging adults (≤ 26 years; n = 439, M = 22.42, SD = 2.16) and older adults (> 26 years; n = 398, M = 39.54, SD = 10.33). The research examined the factorial structure of the CMNI-29 in a Spanish sample, accumulated evidence of its internal validity, and tested its measurement invariance across both age groups. A novel methodological approach was applied, combining Exploratory Graph Analysis (EGA), Confirmatory Factor Analysis (CFA), and Multiple Indicators, Multiple Causes (MIMIC) models to assess changes in measurement due to the transition to adulthood. The theoretical structure was confirmed by EGA and CFA, showing scalar invariance across groups. Furthermore, significant differences in latent means were observed, with the older group reporting lower adherence to norms, particularly in winning, risk-taking, violence, self-reliance, and heterosexual self-presentation. These findings suggest that masculinity ideologies differ between emerging and older adulthood, reflecting shifts in life priorities and social expectations.

Indigenous management theories often seek to evolve from context-specific explanations to broader theoretical contributions. Yet such expansion is frequently undermined by two recurring problems: meaning drift, where construct labels travel while their underlying meaning or measurement shifts, and mechanism slippage, where theories are stretched through post hoc moderators or rotating mediators rather than coherent causal explanations. This paper introduces a Gate-and-Expand framework that builds on Chen’s (2025) theory-evolution map and specifies two checkpoints for credible theoretical expansion. Gate 1 (Construct Portability) evaluates whether a focal construct retains comparable latent meaning and measurement across contexts. Gate 2 (Mechanism Transportability) examines whether a compact, pre-specified causal mechanism continues to explain outcomes and outperforms plausible rival explanations when the theory is extended. By clarifying evidentiary standards for expanding contextual and explanatory scope, the framework helps distinguish credible theoretical extension from overreach and supports more cumulative indigenous management research.

Measurement invariance (MI) ensures that a given measure holds the same conceptual meaning for individuals from different groups and across multiple measurement occasions. Structural invariance (SI) is a logical extension of MI that examines whether relationships between latent constructs (e.g., structural paths within the model) hold equally across groups. To examine the status quo of MI and SI in second-language (L2) research, we systematically investigated the extent to which primary studies adhered to best practices in invariance testing and reporting. A total of 4,272 full-text records were screened, and 113 articles (116 independent samples; 147,856 participants) were included. The sample was fully double-coded to ensure accuracy and reliability. The results indicated alarming inconsistencies in how key invariance steps were implemented and reported. We offer empirically grounded recommendations for (a) improving methodological rigor of invariance assessments in the field and (b) contributing to more equitable and interpretable comparisons in multilingual settings.

Populists believe in the sovereignty of the people. Yet, the people can be construed in ethnic terms or in civic terms. Using a novel wording experiment in Germany (N = 7,034), we examine whether ethnic or civic conceptions of ‘the people’ affect respondents' adherence to key populist attitude items. ‘Volk’ in German has an ethno‐nationalist connotation and has been used throughout history to signify ‘ethnic Germans’. The concept ‘Bürger’, or citizens, by contrast, lacks an ethnic undercurrent. Similar ethnic connotations of ‘the people’ are also common in other languages. We find that there are statistically significant differences between items framed in an ethnic and a civic manner – and that this differs per item. This relationship is significantly moderated by respondents' degree of exclusive national identity and voting behaviour for the radical right. Our findings suggest that the way in which the people is conceptualized has important implications for the measurement of populist attitudes. When populist attitudes are measured with an ethnic understanding of the ‘the people’, the construct is biased towards right‐wing populism, inhibiting the measurement of populism as a ‘thin ideology’. Moreover, we demonstrate the importance of careful translations in comparative research, since some translated synonyms carry different semantic meanings and thereby change the concept under investigation.

Political scientists often need to evaluate whether samples are comparable, for example, when analysing different countries or time points or when comparing data collected using different methods. A necessary condition for conducting such meaningful cross-group comparisons is the establishment of measurement invariance. One of the most frequently used procedures for establishing measurement invariance is the multigroup confirmatory factor analysis. This method was criticised in the literature because it may suggest that a model fits the data although it may contain serious misspecifications. We present an alternative method to test for measurement invariance using detection of local misspecifications and illustrate its use on two data sets assessing value priorities that are often analysed in political science and collected using paper-and-pencil and web modes of data collection.

The aim of the present study was to develop short and very short forms of the Children’s Test Anxiety Scale (CTAS) using the ant colony optimization (ACO) algorithm. The item selection algorithm for the short form was applied to Sample 1 (N = 570), and the best-fitting short form was identified based on validity and reliability evidence. These analyses were then replicated with Sample 2 (N = 825) to confirm the findings. Children’s Perceived Academic Self-Efficacy Scale used for convergent validity. Also measurement invariance tested by gender. Additionally, a very short form of the scale (CTAS-VS) was developed using a subset of the same sample. Across all three studies, consistent results were found in terms of model fit, factor structure and validity. Overall, findings suggest that both the 14-item short form (CTAS-S) and the 3-item very short form (CTAS-VS), developed via the ACO algorithm, possess strong psychometric properties.

The present study investigated the cross-national measurement invariance of a 10-item Youth Externalizing Problems Screener (YEPS) on a sample of 17,489 adolescents from 32 countries. The original one-factor and two-factor models of YEPS were found to provide a poor fit to the data in most countries. Following the removal of two semantically overlapping items and the inclusion of correlated error terms, adequate model fit was obtained in 31 of 32 countries. Measurement invariance testing of an abbreviated 8-item YEPS (YEPS-SF) supported configural invariance. Partial scalar invariance was achieved only after freely estimating numerous parameters. The alignment analysis revealed that 22% of parameters were non-invariant across countries. South Africa, Hungary, and India showed the largest number of non-invariant parameters, whereas the lowest number was detected in several European countries. These findings highlight the potential of the YEPS-SF for use within individual countries and the challenge of developing cross-culturally comparable measures, suggesting that cultural adaptations may be necessary.

In item response theory (IRT), it is often necessary to perform restricted recalibration (RR) of the model: A set of (focal) parameters is estimated holding a set of (nuisance) parameters fixed. Typical applications of RR include expanding an existing item bank, linking multiple test forms, and associating constructs measured by separately calibrated tests. In the current work, we provide full statistical theory for RR of IRT models under the framework of pseudo-maximum likelihood estimation. We describe the standard error calculation for the focal parameters, the assessment of overall goodness-of-fit (GOF) of the model, and the identification of misfitting items. We report a simulation study to evaluate the performance of these methods in the scenario of adding a new item to an existing test. Parameter recovery for the focal parameters as well as Type I error and power of the proposed tests are examined. An empirical example is also included, in which we validate the pediatric fatigue short-form scale in the Patient-Reported Outcome Measurement Information System (PROMIS), compute global and local GOF statistics, and update parameters for the misfitting items.

Penalized factor analysis is an efficient technique that produces a factor loading matrix with many zero elements thanks to the introduction of sparsity-inducing penalties within the estimation process. However, sparse solutions and stable model selection procedures are only possible if the employed penalty is non-differentiable, which poses certain theoretical and computational challenges. This article proposes a general penalized likelihood-based estimation approach for single- and multiple-group factor analysis models. The framework builds upon differentiable approximations of non-differentiable penalties, a theoretically founded definition of degrees of freedom, and an algorithm with integrated automatic multiple tuning parameter selection that exploits second-order analytical derivative information. The proposed approach is evaluated in two simulation studies and illustrated using a real data set. All the necessary routines are integrated into the R package penfa.

A first-order autoregressive growth model is proposed for longitudinal binary item analysis where responses to the same items are conditionally dependent across time given the latent traits. Specifically, the item response probability for a given item at a given time depends on the latent trait as well as the response to the same item at the previous time, or the lagged response. An initial conditions problem arises because there is no lagged response at the initial time period. We handle this problem by adapting solutions proposed for dynamic models in panel data econometrics. Asymptotic and finite sample power for the autoregressive parameters are investigated. The consequences of ignoring local dependence and the initial conditions problem are also examined for data simulated from a first-order autoregressive growth model. The proposed methods are applied to longitudinal data on Korean students’ self-esteem.

The conventional setup for multi-group structural equation modeling requires a stringent condition of cross-group equality of intercepts before mean comparison with latent variables can be conducted. This article proposes a new setup that allows mean comparison without the need to estimate any mean structural model. By projecting the observed sample means onto the space of the common scores and the space orthogonal to that of the common scores, the new setup allows identifying and estimating the means of the common and specific factors, although, without replicate measures, variances of specific factors cannot be distinguished from those of measurement errors. Under the new setup, testing cross-group mean differences of the common scores is done independently from that of the specific factors. Such independent testing eliminates the requirement for cross-group equality of intercepts by the conventional setup in order to test cross-group equality of means of latent variables using chi-square-difference statistics. The most appealing piece of the new setup is a validity index for mean differences, defined as the percentage of the sum of the squared observed mean differences that is due to that of the mean differences of the common scores. By analyzing real data with two groups, the new setup is shown to offer more information than what is obtained under the conventional setup.

Several concepts are introduced and defined: measurement invariance, structural bias, weak measurement invariance, strong factorial invariance, and strict factorial invariance. It is shown that factorial invariance has implications for (weak) measurement invariance. Definitions of fairness in employment/admissions testing and salary equity are provided and it is argued that strict factorial invariance is required for fairness/equity to exist. Implications for item and test bias are developed and it is argued that item or test bias probably depends on the existence of latent variables that are irrelevant to the primary goal of test constructers.

A common assessment research design is the single-group pre-test/post-test design in which examinees are administered an assessment before instruction and then another assessment after instruction. In this type of study, the primary objective is to measure growth in examinees, individually and collectively. In an item response theory (IRT) framework, longitudinal IRT models can be used to assess growth in examinee ability over time. In a diagnostic classification model (DCM) framework, assessing growth translates to measuring changes in attribute mastery status over time, thereby providing a categorical, criterion-referenced interpretation of growth. This study introduces the Transition Diagnostic Classification Model (TDCM), which combines latent transition analysis with the log-linear cognitive diagnosis model to provide methodology for analyzing growth in a general DCM framework. Simulation study results indicate that the proposed model is flexible, provides accurate and reliable classifications, and is quite robust to violations to measurement invariance over time. The TDCM is used to analyze pre-test/post-test data from a diagnostic mathematics assessment.

A multinormal partial credit model for factor analysis of polytomously scored items with ordered response categories is derived using an extension of the Dutch Identity (Holland in Psychometrika 55:5–18, 1990). In the model, latent variables are assumed to have a multivariate normal distribution conditional on unweighted sums of item scores, which are sufficient statistics. Attention is paid to maximum likelihood estimation of item parameters, multivariate moments of latent variables, and person parameters. It is shown that the maximum likelihood estimates can be found without the use of numerical integration techniques. More general models are discussed which can be used for testing the model, and it is shown how models with different numbers of latent variables can be tested against each other. In addition, multi-group extensions are proposed, which can be used for testing both measurement invariance and latent population differences. Models and procedures discussed are demonstrated in an empirical data example.

We present a class of finite mixture multilevel multidimensional ordinal IRT models for large scale cross-cultural research. Our model is proposed for confirmatory research settings. Our prior for item parameters is a mixture distribution to accommodate situations where different groups of countries have different measurement operations, while countries within these groups are still allowed to be heterogeneous. A simulation study is conducted that shows that all parameters can be recovered. We also apply the model to real data on the two components of affective subjective well-being: positive affect and negative affect. The psychometric behavior of these two scales is studied in 28 countries across four continents.

Establishing the invariance property of an instrument (e.g., a questionnaire or test) is a key step for establishing its measurement validity. Measurement invariance is typically assessed by differential item functioning (DIF) analysis, i.e., detecting DIF items whose response distribution depends not only on the latent trait measured by the instrument but also on the group membership. DIF analysis is confounded by the group difference in the latent trait distributions. Many DIF analyses require knowing several anchor items that are DIF-free in order to draw inferences on whether each of the rest is a DIF item, where the anchor items are used to identify the latent trait distributions. When no prior information on anchor items is available, or some anchor items are misspecified, item purification methods and regularized estimation methods can be used. The former iteratively purifies the anchor set by a stepwise model selection procedure, and the latter selects the DIF-free items by a LASSO-type regularization approach. Unfortunately, unlike the methods based on a correctly specified anchor set, these methods are not guaranteed to provide valid statistical inference (e.g., confidence intervals and p-values). In this paper, we propose a new method for DIF analysis under a multiple indicators and multiple causes (MIMIC) model for DIF. This method adopts a minimal \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$L_1$$\end{document} norm condition for identifying the latent trait distributions. Without requiring prior knowledge about an anchor set, it can accurately estimate the DIF effects of individual items and further draw valid statistical inferences for quantifying the uncertainty. Specifically, the inference results allow us to control the type-I error for DIF detection, which may not be possible with item purification and regularized estimation methods. We conduct simulation studies to evaluate the performance of the proposed method and compare it with the anchor-set-based likelihood ratio test approach and the LASSO approach. The proposed method is applied to analysing the three personality scales of the Eysenck personality questionnaire-revised (EPQ-R).