Occupational differences in work engagement: A longitudinal study among eight occupational groups in Norway

The aim of the present study was to explore occupational differences in the experience of engagement both with regard to differences in the level of work engagement as well as in the predicting value of different antecedent variables. Multigroup latent mean analysis was performed on eight different occupational groups in Norway (lawyers, physicians, nurses, teachers, church ministers, bus drivers, and people working in advertising and information technology; N = 3,475). Tests for factorial invariance supported the use of the Oldenburg Burnout Inventory scale across occupational groups and that the latent means were comparable across the groups. Results indicated significant occupational differences in the experience of vigor and dedication. The lawyers reported the most vigor and the church ministers the most dedication. Least vigor was reported among the teachers and the advertising group reported to be least dedicated. Cross‐lagged multigroup structural equation modeling (SEM) analysis suggested there are different processes behind the development of engagement across occupations. Visualization of how some occupations cluster or differ from each other is important as it might engender theory building and further hypotheses testing.     

对齐法在单因子和三因子多组比较中允许的不等参数率

在传统的多组验证性因子分析中,进行因子均值比较的前提条件是模型满足强测量不变性假设,但该假设在实证研究中很难满足。这时,Asparouhov和Muthén(2014)提出的对齐法是一种可供备选的多组分析法。通过蒙特卡洛模拟研究,探究了在对齐法精确估计模型参数前提下所允许的不等参数率范围。研究发现：在平均组样本量充足,识别算法为固定识别算法等理想状态下,对齐法在单因子模型多组比较中所允许的不等参数率可以是100%;在三因子模型中所允许的不等参数率上限是20%至30%;在此范围内,对齐法允许更多高程度不等参数;在组数目从30、15或9降至3时,对齐法能允许更多的不等参数在模型中。     

Is Measurement Noninvariance a Threat to Inferences Drawn from Randomized Control Trials? Evidence From Empirical and Simulation Studies

Randomized control trials (RCTs) are considered the gold standard when evaluating the impact of psychological interventions, educational programs, and other treatments on outcomes of interest. However, few studies consider whether forms of measurement bias like noninvariance might impact estimated treatment effects from RCTs. Such bias may be more likely to occur when survey scales are utilized in studies and evaluations in ways not supported by validation evidence, which occurs in practice. This study consists of simulation and empirical studies examining whether measurement noninvariance impacts treatment effects from RCTs. Simulation study results demonstrate that bias in treatment effect estimates is mild when the noninvariance occurs between subgroups (e.g., male and female participants), but can be quite substantial when being assigned to control or treatment induces the noninvariance. Results from the empirical study show that surveys used in two federally funded evaluations of educational programs were noninvariant across student age groups.     

Development of the Inventory of College Students' Resilience and evaluating the measurement invariance

In order to help students adapt well to stressful situations or adversity, many researchers have explored the issue of students' resilience. The main purpose of this study was to develop the Inventory of College Students' Resilience (ICSR), for assessing resilience of college students. The present study recruited 993 participants to conduct item analysis, exploratory factor analysis and reliability analysis. We then administered the questionnaire to another 1490 participants in order to conduct confirmatory factor analysis and assess criterion-related validity. The results showed the proposed model fit the data well and the ICSR had both internal consistency and criterion-related validity. The ICSR was also found to have measurement invariance across gender. We conclude that the ICSR can help students to identify strengths and weaknesses in order to reinforce their resilience and improve their response to life stress and trauma.     

Consequences of Ignoring Guessing Effects on Measurement Invariance Analysis

Pseudo-guessing parameters are present in item response theory applications for many educational assessments. When sample size is not sufficiently large, the guessing parameters may be ignored from the analysis. This study examines the impact of ignoring pseudo-guessing parameters on measurement invariance analysis, specifically, on item difficulty, item discrimination, and mean and variance of ability distribution. Results show that when non-zero guessing parameters are ignored from the measurement invariance analysis, item discrimination estimates tend to decrease particularly for more difficult items, and item difficulty estimates decrease unless the items are highly discriminating and difficult. As the guessing parameter increases, the size of the decrease in item discrimination and difficulty tends to increase, and the estimated mean and variance of ability distribution tend to be inaccurate. When two groups have heterogeneous ability distributions, ignoring the guessing parameter affects the reference group and the focal group differently. Implications of result findings are discussed.     

Confirmatory Factor Analysis (CFA), Exploratory Structural Equation Modeling (ESEM), and Set-ESEM: Optimal Balance Between Goodness of Fit and Parsimony

Abstract

CFAs of multidimensional constructs often fail to meet standards of good measurement (e.g., goodness-of-fit, measurement invariance, and well-differentiated factors). Exploratory structural equation modeling (ESEM) represents a compromise between exploratory factor analysis’ (EFA) flexibility, and CFA/SEM’s rigor and parsimony, but lacks parsimony (particularly in large models) and might confound constructs that need to be kept separate. In Set-ESEM, two or more a priori sets of constructs are modeled within a single model such that cross-loadings are permissible within the same set of factors (as in Full-ESEM) but are constrained to be zero for factors in different sets (as in CFA). The different sets can reflect the same set of constructs on multiple occasions, and/or different constructs measured within the same wave. Hence, Set-ESEM that represents a middle-ground between the flexibility of traditional-ESEM (hereafter referred to as Full-ESEM) and the rigor and parsimony of CFA/SEM. Thus, the purposes of this article are to provide an overview tutorial on Set-ESEM, juxtapose it with Full-ESEM, and to illustrate its application with simulated data and diverse “real” data applications with accessible, heuristic explanations of best practice.     

Factorial validity of the Utrecht Work Engagement Scale (UWES) across occupational groups in Norway

Nerstad, C.G.L., Richardsen, A.M. & Martinussen, M. (2009). Factorial validity of the Utrecht Work Engagement Scale (UWES) across occupational groups in Norway. Scandinavian Journal of Psychology. The present study investigated the factorial validity of the Utrecht Work Engagement Scale (UWES) among 1266 participants from ten different occupational groups. Confirmatory factor analyses of the total sample, as well as multi‐group analyses and analyses of each of the ten occupational groups separately, indicated that a three‐dimensional model of both the UWES‐17 and the short version, UWES‐9, provided a better fit to the data than a one‐ and two‐dimensional model. The results of multi‐group analyses and analyses of each of the groups separately, indicated that with a few exceptions, the three‐factor model of work engagement provided the best fit. Results indicated factorial invariance and the internal consistencies were acceptable. The fit of the UWES‐9 was slightly better than the UWES‐17. It is concluded that the Norwegian short version may be recommended over the UWES‐17.     

Testing Differential Item Functioning in Small Samples

Abstract

Differential item functioning (DIF) is a pernicious statistical issue that can mask true group differences on a target latent construct. A considerable amount of research has focused on evaluating methods for testing DIF, such as using likelihood ratio tests in item response theory (IRT). Most of this research has focused on the asymptotic properties of DIF testing, in part because many latent variable methods require large samples to obtain stable parameter estimates. Much less research has evaluated these methods in small sample sizes despite the fact that many social and behavioral scientists frequently encounter small samples in practice. In this article, we examine the extent to which model complexity—the number of model parameters estimated simultaneously—affects the recovery of DIF in small samples. We compare three models that vary in complexity: logistic regression with sum scores, the 1-parameter logistic IRT model, and the 2-parameter logistic IRT model. We expected that logistic regression with sum scores and the 1-parameter logistic IRT model would more accurately estimate DIF because these models yielded more stable estimates despite being misspecified. Indeed, a simulation study and empirical example of adolescent substance use show that, even when data are generated from / assumed to be a 2-parameter logistic IRT, using parsimonious models in small samples leads to more powerful tests of DIF while adequately controlling for Type I error. We also provide evidence for minimum sample sizes needed to detect DIF, and we evaluate whether applying corrections for multiple testing is advisable. Finally, we provide recommendations for applied researchers who conduct DIF analyses in small samples.     

Scale horizons in analytic hierarchies

In the light of the many criticisms to which the analytic hierarchy process (AHP) of Saaty has been subjected, the authors have produced a refined version, the modified analytic hierarchy process. This paper develops further the ideas behind the modified process and illustrates with examples some of the difficulties in the AHP and their resolution.