Investigation of measurement invariance (MI) commonly assumes correct specification of dimensionality across multiple groups. Although research shows that violation of the dimensionality assumption can cause bias in model parameter estimation for single-group analyses, little research on this issue has been conducted for multiple-group analyses. This study explored the effects of mismatch in dimensionality between data and analysis models with multiple-group analyses at the population and sample levels. Datasets were generated using a bifactor model with different factor structures and were analyzed with bifactor and single-factor models to assess misspecification effects on assessments of MI and latent mean differences. As baseline models, the bifactor models fit data well and had minimal bias in latent mean estimation. However, the low convergence rates of fitting bifactor models to data with complex structures and small sample sizes caused concern. On the other hand, effects of fitting the misspecified single-factor models on the assessments of MI and latent means differed by the bifactor structures underlying data. For data following one general factor and one group factor affecting a small set of indicators, the effects of ignoring the group factor in analysis models on the tests of MI and latent mean differences were mild. In contrast, for data following one general factor and several group factors, oversimplifications of analysis models can lead to inaccurate conclusions regarding MI assessment and latent mean estimation.

Researchers who conduct longitudinal studies are inherently interested in studying individual and population changes over time (e.g., mathematics achievement, subjective well-being). To answer such research questions, models of change (e.g., growth models) make the assumption of longitudinal measurement invariance. In many applied situations, key constructs are measured by a collection of ordered-categorical indicators (e.g., Likert scale items). To evaluate longitudinal measurement invariance with ordered-categorical indicators, a set of hierarchical models can be sequentially tested and compared. If the statistical tests of measurement invariance fail to be supported for one of the models, it is useful to have a method with which to gauge the practical significance of the differences in measurement model parameters over time. Drawing on studies of latent growth models and second-order latent growth models with continuous indicators (e.g., Kim & Willson, 2014a; 2014b; Leite, 2007; Wirth, 2008), this study examined the performance of a potential sensitivity analysis to gauge the practical significance of violations of longitudinal measurement invariance for ordered-categorical indicators using second-order latent growth models. The change in the estimate of the second-order growth parameters following the addition of an incorrect level of measurement invariance constraints at the first-order level was used as an effect size for measurement non-invariance. This study investigated how sensitive the proposed sensitivity analysis was to different locations of non-invariance (i.e., non-invariance in the factor loadings, the thresholds, and the unique factor variances) given a sufficient sample size. This study also examined whether the sensitivity of the proposed sensitivity analysis depended on a number of other factors including the magnitude of non-invariance, the number of non-invariant indicators, the number of non-invariant occasions, and the number of response categories in the indicators.

Individuals are attracted to occupational environments that align with their interests and personality characteristics (Holland, 1985, 1997). When an individual's attributes (i.e., needs, abilities, values and/or interests) align with the occupational environment's characteristics an individual is more satisfied. Past research suggests this relation is modest at best, hovering between .20 and .30 (Meyer et al., 2001, c.f. Wilkins & Tracey, 2014), with slightly higher estimates (ranging from .24 to .35) depending on how the variables of person and environment were measured (Kristof-Brown, Zimmerman, & Johnson, 2005). Several factors contribute to such low estimates, most notably the role of moderator variables in suppressing or exacerbating the true magnitude of this relation. A moderator that has yet to be explored is that of nonwork role priority, or the degree to which an individual's work identity is valued relative to other role identities. In the current study, three hypotheses were posited to investigate nonwork role priorities as a potential moderator to the congruence-satisfaction relation. Latent class analysis was used to apply a person-centered approach to understanding response patterns and differences in these roles. The sample was differentiated best by a two-class solution and the class variable in all three hierarchical regression models explained about five percent of the variance in job satisfaction, which suggests that work and nonwork role priority are meaningful to understanding individual career happiness. Class was not identified as a significant moderator to the congruence-satisfaction relation. Discussion of limitations to the current study and recommendations for future work in this area are presented.

A simulation study was conducted to explore the influence of partial loading invariance and partial intercept invariance on the latent mean comparison of the second-order factor within a higher-order confirmatory factor analysis (CFA) model. Noninvariant loadings or intercepts were generated to be at one of the two levels or both levels for a second-order CFA model. The numbers and directions of differences in noninvariant loadings or intercepts were also manipulated, along with total sample size and effect size of the second-order factor mean difference. Data were analyzed using correct and incorrect specifications of noninvariant loadings and intercepts. Results summarized across the 5,000 replications in each condition included Type I error rates and powers for the chi-square difference test and the Wald test of the second-order factor mean difference, estimation bias and efficiency for this latent mean difference, and means of the standardized root mean square residual (SRMR) and the root mean square error of approximation (RMSEA).

When the model was correctly specified, no obvious estimation bias was observed; when the model was misspecified by constraining noninvariant loadings or intercepts to be equal, the latent mean difference was overestimated if the direction of the difference in loadings or intercepts of was consistent with the direction of the latent mean difference, and vice versa. Increasing the number of noninvariant loadings or intercepts resulted in larger estimation bias if these noninvariant loadings or intercepts were constrained to be equal. Power to detect the latent mean difference was influenced by estimation bias and the estimated variance of the difference in the second-order factor mean, in addition to sample size and effect size. Constraining more parameters to be equal between groups—even when unequal in the population—led to a decrease in the variance of the estimated latent mean difference, which increased power somewhat. Finally, RMSEA was very sensitive for detecting misspecification due to improper equality constraints in all conditions in the current scenario, including the nonzero latent mean difference, but SRMR did not increase as expected when noninvariant parameters were constrained.

Research suggests that some children with primary language impairment (PLI)

have difficulty with certain aspects of executive function; however, most studies examining executive function have been conducted using tasks that require children to use language to complete the task. As a result, it is unclear whether poor performance on executive function tasks was due to language impairment, to executive function deficits, or both. The purpose of this study is to evaluate whether preschoolers with PLI have deficits in executive function by comprehensively examining inhibition, updating, and mental set shifting using tasks that do and do not required language to complete the tasks.

Twenty-two four and five-year-old preschoolers with PLI and 30 age-matched preschoolers with typical development (TD) completed two sets of computerized executive function tasks that measured inhibition, updating, and mental set shifting. The first set of tasks were language based and the second were visually-based. This permitted us to test the hypothesis that poor performance on executive function tasks results from poor executive function rather than language impairment. A series of one-way analyses of covariance (ANCOVAs) were completed to test whether there was a significant between-group difference on each task after controlling for attention scale scores. In each analysis the between-group factor was group and the covariate was attention scale scores.

Results showed that preschoolers with PLI showed difficulties on a broad range of linguistic and visual executive function tasks even with scores on an attention measure covaried. Executive function deficits were found for linguistic inhibition, linguistic and visual updating, and linguistic and visual mental set shifting. Overall, findings add to evidence showing that the executive functioning deficits of children with PLI is not limited to the language domain, but is more general in nature. Implications for early assessment and intervention will be discussed.

The relationships between goals and specific flirting behaviors were investigated in a college population. Research questions and hypotheses were guided by Dillard's (1990) Goals-Plans-Action (GPA) model of interpersonal influence, which states that goals lead to planning processes, which, in turn, produce behavior. Six hundred and eighty-five undergraduates at a large southwestern university participated in an online survey assessing their behaviors in their most recent flirting interactions, their goals for that interaction, as well as measures designed to assess planning, the importance of the goal, and the number of goals present for the interaction. Results indicate that goals relate to the use of some, but not all behaviors, and that a flirting script may exist. Furthermore, planning, importance, and number of goals were all found to relate to the reporting of specific flirting behaviors. Sex differences were found as well, such that men reported using more forward and direct behaviors, while women reported using more facial expressions, self-touch, and laughing; men also reported flirting for sexual reasons more than women, and women reported flirting for more fun reasons that men. Overall, this study confirms the utility of the GPA framework for understanding the relationship between goals and flirting behavior, and suggests several avenues for future research.

Structural equation modeling is potentially useful for assessing mean differences between groups on latent variables (i.e., factors). However, to evaluate these differences accurately, the parameters of the indicators of these latent variables must be specified correctly. The focus of the current research is on the specification of between-group equality constraints on the loadings and intercepts of indicators. These equality constraints are referred to as invariance constraints. Previous simulation studies in this area focused on fitting a particular model to data that were generated to have various levels and patterns of non-invariance. Results from these studies were interpreted from a viewpoint of assumption violation rather than model misspecification. In contrast, the current study investigated analysis models with varying number of invariance constraints given data that were generated based on a model with indicators that were invariant, partially invariant, or non-invariant. More broadly, the current simulation study was conducted to examine the effect of correctly or incorrectly imposing invariance constraints as well as correctly or incorrectly not imposing invariance constraints on the assessment of factor mean differences. The results indicated that different types of analysis models yield different results in terms of Type I error rates, power, bias in estimation of factor mean difference, and model fit. Benefits and risks are associated with imposing or reducing invariance constraints on models. In addition, model fit or lack of fit can lead to wrong decisions concerning invariance constraints.

The measurement of competency in nursing is critical to ensure safe and effective care of patients. This study had two purposes. First, the psychometric characteristics of the Nursing Performance Profile (NPP), an instrument used to measure nursing competency, were evaluated using generalizability theory and a sample of 18 nurses in the Measuring Competency with Simulation (MCWS) Phase I dataset. The relative magnitudes of various error sources and their interactions were estimated in a generalizability study involving a fully crossed, three-facet random design with nurse participants as the object of measurement and scenarios, raters, and items as the three facets. A design corresponding to that of the MCWS Phase I data--involving three scenarios, three raters, and 41 items--showed nurse participants contributed the greatest proportion to total variance (50.00%), followed, in decreasing magnitude, by: rater (19.40%), the two-way participant x scenario interaction (12.93%), and the two-way participant x rater interaction (8.62%). The generalizability (G) coefficient was .65 and the dependability coefficient was .50. In decision study designs minimizing number of scenarios, the desired generalizability coefficients of .70 and .80 were reached at three scenarios with five raters, and five scenarios with nine raters, respectively. In designs minimizing number of raters, G coefficients of .72 and .80 were reached at three raters and five scenarios and four raters and nine scenarios, respectively. A dependability coefficient of .71 was attained with six scenarios and nine raters or seven raters and nine scenarios. Achieving high reliability with designs involving fewer raters may be possible with enhanced rater training to decrease variance components for rater main and interaction effects. The second part of this study involved the design and implementation of a validation process for evidence-based human patient simulation scenarios in assessment of nursing competency. A team of experts validated the new scenario using a modified Delphi technique, involving three rounds of iterative feedback and revisions. In tandem, the psychometric study of the NPP and the development of a validation process for human patient simulation scenarios both advance and encourage best practices for studying the validity of simulation-based assessments.

This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex performance assessment within a digital-simulation educational context grounded in theories of cognition and learning. BN models were manipulated along two factors: latent variable dependency structure and number of latent classes. Distributions of posterior predicted p-values (PPP-values) served as the primary outcome measure and were summarized in graphical presentations, by median values across replications, and by proportions of replications in which the PPP-values were extreme. An effect size measure for PPMC was introduced as a supplemental numerical summary to the PPP-value. Consistent with previous PPMC research, all investigated fit functions tended to perform conservatively, but Standardized Generalized Dimensionality Discrepancy Measure (SGDDM), Yen's Q3, and Hierarchy Consistency Index (HCI) only mildly so. Adequate power to detect at least some types of misfit was demonstrated by SGDDM, Q3, HCI, Item Consistency Index (ICI), and to a lesser extent Deviance, while proportion correct (PC), a chi-square-type item-fit measure, Ranked Probability Score (RPS), and Good's Logarithmic Scale (GLS) were powerless across all investigated factors. Bivariate SGDDM and Q3 were found to provide powerful and detailed feedback for all investigated types of misfit.