Factorial Invariance in a Repeated Measures Design: An Application to the Study of Person-Organization Fit

2010 ◽  
Vol 13 (1) ◽  
pp. 485-493 ◽  
Author(s):  
Carmen Ximénez ◽  
Javier Revuelta
Keyword(s):  
Measurement Invariance ◽  
Repeated Measures ◽  
Point Of View ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Factor Loadings ◽  
First Order ◽  
Repeated Measures Design ◽  
Order Factor ◽  
Person Organization Fit

An important methodological concern of any research based on a person-environment (P-E) fit approach is the operationalization of the fit, which imposes some measurement requirements that are rarely empirically tested with statistical methods. Among them, the assessment of the P and E components along commensurate dimensions is possibly the most cited one. This paper proposes to test the equivalence across the P and E measures by analyzing the measurement invariance of a multi-group confirmatory factor analysis model. From a methodological point of view, the distinct aspect of this approach within the context of P-E fit research is that measurement invariance is assessed in a repeated measures design. An example illustrating the procedure in a person-organization (P-O) fit dataset is provided. Measurement invariance was tested at five different hierarchical levels: (1) configural, (2) first-order factor loadings, (3) second-order factor loadings, (4) residual variances of observed variables, and (5) disturbances of first-order factors. The results supported the measurement invariance across the P and O measures at the third level. The implications of these findings for P-E fit studies are discussed.

A Second-Order Factor Analysis Model for Measuring Tourists' Overall Image of Algarve, Portugal

2005 ◽  
Vol 11 (4) ◽  
pp. 539-554 ◽  
Author(s):  
António Luís Silvestre ◽  
Antónia Correia
Keyword(s):  
Factor Analysis ◽  
Important Determinant ◽  
Second Order ◽  
The Sun ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Convenience Sample ◽  
First Order ◽  
Order Factor ◽  
Second Order Factor Analysis

Algarve is a tourism region in the south of Portugal. This paper develops and empirically validates a second-order factor analysis model to assess the overall image of Algarve held by tourists who visit it. The data are based on the opinions of a random convenience sample of tourists taken at Faro Airport. It is found that the observable variables define three first-order factors – that is, three image factors – and these are used as indicators of a unique second-order factor, which is the overall image held by tourists to Algarve. The main conclusion of the paper is that the ‘sun and sand’ factor is the most important determinant of tourists' overall image of the region.

scholarly journals Measurement Invariance of the Career Futures Inventory–Revised Across General and Client Samples

2018 ◽  
Vol 27 (4) ◽  
pp. 711-725
Author(s):  
Chan Jeong Park ◽  
Patrick J. Rottinghaus ◽  
Ze Wang ◽  
Ti Zhang ◽  
Nikki A. Falk ◽  
...  
Keyword(s):  
Measurement Invariance ◽  
Scale Validation ◽  
Career Adaptability ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Student Sample ◽  
Configural Invariance ◽  
Validation Procedure ◽  
Scalar Invariance ◽  
Latent Means

Establishing measurement invariance has been emphasized as an important scale validation procedure for group comparisons. The 28-item Career Futures Inventory–Revised (CFI-R) is a widely used measure of career adaptability that has demonstrated initial validity with various samples. The purpose of the present study is to further examine the validity of the CFI-R by testing measurement invariance between a general university student sample and a client sample. First, a five-factor confirmatory factor analysis model was tested with each group. Then, measurement invariance tests were conducted through subsequently examining configural invariance, metric invariance, and scalar invariance. Test of invariance was achieved until partial scalar invariance, suggesting that the CFI-R is similarly applicable to both clinical and nonclinical samples. In addition, the comparisons of latent means between two groups revealed that clients showed significantly lower latent means than general students for four factors: Career Agency, Occupational Awareness, Support, and Work–Life Balance.

The Adolescent Peer Relations Instrument- Bully/Target: Measurement Invariance Across Gender, Age, and Clinical Status

2020 ◽  
pp. 088626052092235
Author(s):  
Raluca Balan ◽  
Anca Dobrean ◽  
Robert Balazsi ◽  
Roberto H. Parada ◽  
Elena Predescu
Keyword(s):  
Measurement Invariance ◽  
Peer Relations ◽  
Clinical Status ◽  
Factorial Structure ◽  
Physical Victimization ◽  
First Order ◽  
Verbal Bullying ◽  
Order Factor ◽  
And Gender ◽  
Physical Bullying

Adolescent Peer Relations Instrument–Bully/Target (APRI-BT) is a multidimensional scale designed to assess bullying involvement both as target and perpetrator. Although existing research has shown that the APRI-BT satisfies the assumption of measurement invariance across age and gender, these findings come from western individualistic countries (e.g., Australia). This study aimed to investigate the factorial structure and measurement invariance across age, gender, and clinical status in a sample of Romanian youths. Participants were 1,024 adolescents, 10 to 18 years, recruited from both community and clinical setting. Our results confirmed a six first-order factor structure and two second-order factors (Bully including Bullying Physical, Bullying Verbal, Bullying Social and Victimization including Physical Victimization, Verbal Victimization, Social Victimization). In addition, measurement invariance across age, gender, and clinical status was demonstrated. This study identifies APRI-BT as an instrument with solid psychometric proprieties for measuring bullying and victimization among preadolescents and adolescents.

Challenging the Multidimensional Conception of Perceived Person-Environment Fit

2020 ◽  
pp. 1-9
Author(s):  
Julian M. Etzel ◽  
Gabriel Nagy
Keyword(s):  
Higher Education ◽  
University Students ◽  
Educational Outcomes ◽  
Factor Model ◽  
Higher Order ◽  
Substantial Proportion ◽  
Unique Variance ◽  
First Order ◽  
Person Environment Fit ◽  
Order Factor

Abstract. In the current study, we examined the viability of a multidimensional conception of perceived person-environment (P-E) fit in higher education. We introduce an optimized 12-item measure that distinguishes between four content dimensions of perceived P-E fit: interest-contents (I-C) fit, needs-supplies (N-S) fit, demands-abilities (D-A) fit, and values-culture (V-C) fit. The central aim of our study was to examine whether the relationships between different P-E fit dimensions and educational outcomes can be accounted for by a higher-order factor that captures the shared features of the four fit dimensions. Relying on a large sample of university students in Germany, we found that students distinguish between the proposed fit dimensions. The respective first-order factors shared a substantial proportion of variance and conformed to a higher-order factor model. Using a newly developed factor extension procedure, we found that the relationships between the first-order factors and most outcomes were not fully accounted for by the higher-order factor. Rather, with the exception of V-C fit, all specific P-E fit factors that represent the first-order factors’ unique variance showed reliable and theoretically plausible relationships with different outcomes. These findings support the viability of a multidimensional conceptualization of P-E fit and the validity of our adapted instrument.

Analyzing Observed Composite Differences Across Groups

Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Holger Steinmetz
Keyword(s):  
Monte Carlo ◽  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Monte Carlo Study ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Latent Mean Differences ◽  
Partial Invariance ◽  
Mean Differences

Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite’s mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte-Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.

Is That Measure Really One-Dimensional?

Methodology ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 188-196 ◽  
Author(s):  
Esther T. Beierl ◽  
Markus Bühner ◽  
Moritz Heene
Keyword(s):  
Model Fit ◽  
Nuisance Parameters ◽  
Factorial Validity ◽  
Fit Indices ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
One Dimensional ◽  
Confirmatory Factor Analysis Model ◽  
Cutoff Values ◽  
Confirmatory Factor

Abstract. Factorial validity is often assessed using confirmatory factor analysis. Model fit is commonly evaluated using the cutoff values for the fit indices proposed by Hu and Bentler (1999) . There is a body of research showing that those cutoff values cannot be generalized. Model fit does not only depend on the severity of misspecification, but also on nuisance parameters, which are independent of the misspecification. Using a simulation study, we demonstrate their influence on measures of model fit. We specified a severe misspecification, omitting a second factor, which signifies factorial invalidity. Measures of model fit showed only small misfit because nuisance parameters, magnitude of factor loadings and a balanced/imbalanced number of indicators per factor, also influenced the degree of misfit. Drawing from our results, we discuss challenges in the assessment of factorial validity.

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

Methodology ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Pablo Livacic-Rojas ◽  
Guillermo Vallejo ◽  
Paula Fernández ◽  
Ellián Tuero-Herrero
Keyword(s):  
Repeated Measures ◽  
Statistical Power ◽  
Mixed Model ◽  
Covariance Structure ◽  
Simulation Method ◽  
Future Research ◽  
Repeated Measures Design ◽  
Fixed And Random Effects ◽  
Split Plot ◽  
High Level

Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

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