Review of Growth Modeling: Structural Equation and Multilevel Modeling Approaches (Grimm,Ram & Estabrook, 2017)

Research questions that address developmental processes are becoming more prevalent in psychology and other areas of social science. Growth models have become a popular tool to model multiple individuals measured over several time points. These types of models allow researchers to answer a wide variety of research questions, such as modeling inter- and intra-individual differences and variability in longitudinal process (Molenaar 2004). The recently published book, Growth Modeling: Structural Equation and Multilevel Modeling Approaches (Grimm, Ram & Estabrook 2017), provides a solid foundation for both beginners and more advanced researchers interested in longitudinal data analysis by juxtaposing both the multilevel and structural equation modeling frameworks for several different models. By providing both sufficient technical background and practical coding examples in a variety of both commercial and open-source software, this book should serve as an excellent reference tool for behavioral and methodological researchers interested in growth modeling.

     

Predicting Health-related Quality of Life: Testing the Contextual Model Using Structural Equation Modeling

This study aims to investigate the utility of the Contextual Model of Health-Related Quality of Life (HRQOL) to explain the relationship among the domains of HRQOL with a diverse, population-based sample of breast cancer survivors (BCS). We employed a cross-sectional design to investigate HRQOL among 703 multiethnic, population-based BCS. The study methodology was guided by the Contextual Model of HRQOL. Structural Equation Modeling (SEM) was conducted to assess the hypothesized model. SEM identified significant relationships among the bio-psychological domain (general health status, cancer-related factors, and psychological factors), the cultural-socio-ecological domain (health care satisfaction, socio-ecological factor, and socio-economic status), and HRQOL. The best fitting model indicates direct pathways from ‘general health status’, ‘years since diagnosis’, ‘health care satisfaction’ and ‘socio-ecological factor’ to ‘HRQOL’ variables. Additionally, ‘socio-ecological factor’ and ‘socio-economic status’ variables were indirectly associated with HRQOL through ‘general health status’. Findings suggest that the Contextual Model of HRQOL adds valid factors to explain overall HRQOL and increases our understanding of the socio-ecological dimensions predicting HRQOL outcomes. The revelation of inter-relations among the dimensions of HRQOL may inform the translational and clinical utility of the HRQOL construct.

Testing Factorial Structure and Validity of the PCL:SV in Lithuanian Prison Population

This study examined the factorial structure and validity of the Hare Psychopathy Checklist: Screening Version (PCL:SV) in a European forensic context. A random sample of 257 criminal offenders from the Lithuanian prison population were administered the PCL:SV (Hart et al. 1995). The pattern of validity coefficients in this sample was comparable to other North American and non-North American samples. Several theoretical factorial structures for the PCL:SV were tested and compared. The two-factor model was not supported, while three- and four-factor models provided an acceptable fit to Lithuanian data. The four-factor model showed significantly better fit compared to the two-factor model. Correlations between PCL:SV factors and demographic variables confirm construct validity. However in the four-factor model, when controlling for correlations of the remaining three factors, only the Antisocial factor significantly correlated with variables related to criminal activity. It was concluded that the overall results of the PCL:SV in a male sample of Lithuanian offenders support cross-cultural generalizability of the construct of psychopathy as measured by the PCL:SV.     

A comparison of the LISREL and EQS programs for obtaining parameter estimates in confirmatory factor analysis studies

Recently, researchers in psychology have been provided with the techniques for confirming factor analytic structures. Many software packages are now available that provide the researcher with the ability to test factor structures and measurement models with some precision. However, the literature fails to provide the researcher with any comparison of the available software. This paper reviews the basic premise of confirmatory factor analysis and describes the use of two of the more popular software packages (e.g., LISREL and EQS) for obtaining parameter estimates. The two programs are then compared for similarity in estimates and ease of use.     

Abstract: Evaluation of Test Statistics for Robust Structural Equation Modeling With Nonnormal Missing Data

Traditional structural equation modeling (SEM) techniques have trouble dealing with incomplete and/or nonnormal data that are often encountered in practice. Yuan and Zhang (2011a) developed a two-stage procedure for SEM to handle nonnormal missing data and proposed four test statistics for overall model evaluation. Although these statistics have been shown to work well with complete data, their performance for incomplete data has not been investigated in the context of robust statistics.

Focusing on a linear growth curve model, a systematic simulation study is conducted to evaluate the accuracy of the parameter estimates and the performance of five test statistics including the naive statistic derived from normal distribution based maximum likelihood (ML), the Satorra-Bentler scaled chi-square statistic (RML), the mean- and variance-adjusted chi-square statistic (AML), Yuan-Bentler residual-based test statistic (CRADF), and Yuan-Bentler residual-based F statistic (RF). Data are generated and analyzed in R using the package rsem (Yuan & Zhang, 2011b).

Based on the simulation study, we can observe the following: (a) The traditional normal distribution-based method cannot yield accurate parameter estimates for nonnormal data, whereas the robust method obtains much more accurate model parameter estimates for nonnormal data and performs almost as well as the normal distribution based method for normal distributed data. (b) With the increase of sample size, or the decrease of missing rate or the number of outliers, the parameter estimates are less biased and the empirical distributions of test statistics are closer to their nominal distributions. (c) The ML test statistic does not work well for nonnormal or missing data. (d) For nonnormal complete data, CRADF and RF work relatively better than RML and AML. (e) For missing completely at random (MCAR) missing data, in almost all the cases, RML and AML work better than CRADF and RF. (f) For nonnormal missing at random (MAR) missing data, CRADF and RF work better than AML. (g) The performance of the robust method does not seem to be influenced by the symmetry of outliers.     

A Latent-Variable Modeling Approach to Assessing Reliability and Validity of a Creativity Instrument

ABSTRACT: A multiple-choice paper-and-pencil test was developed based on the constructs from the Torrance Test of Creative Thinking (TTCT). The rationale of this new instrument was to shorten the amount of time required for the administration and scoring of creativity tests. This instrument was translated into Spanish and administered along with the TTCT and the Villa and Auzmendi Creativity Test (VAT) to 2,270 students in Spain. Teachers' ratings of student creativity were also obtained. Using a traditional concurrent validation approach from an earlier study, we found low but significant correlations between the scores of the four subscales of the new test and the same subscales of the VAT and TTCT, as well as the students' academic achievement measures. In terms of reliability, mostly moderate (internal consistency) reliability coefficients were found for the new test. As an alternative to the traditional approach, this study employed structural equation modeling (SEM) with multiple indicators to examine the validity and reliability of the new creativity test. The results indicated that substantial improvements in the validity and reliability estimation over the traditional approaches can be made by using SEM with multiple indicators.     

Psychometric Properties of the Gaze Anxiety Rating Scale: Convergent,Discriminant, and Factorial Validity

Fear and avoidance of gaze are two features thought to be associated with problematic social anxiety. Avoidance of eye contact has been linked with such undesirable traits as deceptiveness, insincerity, and lower self-esteem. The Gaze Anxiety Rating Scale (GARS) is a self-report measure designed to assess gaze anxiety and avoidance, but its psychometric properties have only been assessed in one preliminary study. We further investigated psychometric properties of the GARS by assessing convergent and factorial validity. We obtained a two-factor solution: gaze anxiety and avoidance across situations (1) in general (GARS-General) and (2) related to dominance communication (GARS-Dominance). The GARS-General factor related more strongly to social anxiety than the GARS-Dominance, and convergent validity of the factors was supported by expected relationships with personality and social anxiety variables. Our results indicate that the GARS subscales are psychometrically valid measures of gaze aversion, supporting their use in future study of the relationship between social anxiety and eye contact behavior.     

Assessing the Factorial Validity of the Attitudes and Belief Scale 2-Abbreviated Version: A Call for the Development a Gold Standard Method of Measuring Rational and Irrational Beliefs

Rational emotive behaviour therapy (REBT) does not possess a measure of rational and irrational beliefs that meets internationally recognised standards for acceptable psychometric properties. Without such a measure the theory/practice of REBT cannot be rigorously evaluated, thus undermining its scientific veracity. The current study investigates the validity and reliability of a recently developed measure of rational and irrational beliefs: the Attitudes and Belief Scale 2-Abbreviated Version (ABS-2-AV). University students from three countries completed the ABS-2-AV (N = 397). An alternative models framework using confirmatory factor analysis indicated that a theoretically consistent eight-factor model of the ABS-2-AV provided the best fit of the data. A number of post hoc modifications were required in order to achieve acceptable model fit results, and these modifications revealed important methodological limitations with the ABS-2-AV. Results indicated that the validity of the ABS-2-AV was undermined due to items measuring both the psychological process of interest (rational and irrational beliefs) and the context in which these beliefs processes are presented. This is a serious methodological limitation of the ABS-2 and all questionnaires derived from it, including the ABS-2-AV. This methodological limitation resulted in the ABS-2-AV possessing poor internal reliability. These limitations are discussed in relation to the broader REBT literature and the impact such problems have on research and practice. A call is made for REBT researchers to come together to develop a “gold standard” method of assessing rational and irrational beliefs that meets international standard for psychometric excellence.     

Factorial Validity of the Cooperative Preschool Inventory for English- and Spanish-Speaking Hispanic Children

The Cooperative Preschool Inventory (CPI) in both English and Spanish was administered to preschool Hispanic children who were monolingual in either English or Spanish. Subscales were factor-analyzed separately. When the factors were compared using coefficients of congruence, we concluded that both measures appeared to assess the same underlying construct.     

The Effects of Leader Communication on a Worker's Intent to Stay: An Investigation Using Structural Equation Modeling

This study presents findings on the link between leader motivating language (ML) use and worker intent to stay. Structural equation modeling indicated that ML use significantly improves worker intent to stay—with a 10% increase in ML leading to an approximate 5% increase in worker intent to stay. Also, analysis showed that the full ML model better describes the data than any partial model based on a subset of the ML components, and this outcome helps advance researchers' understanding of the ML theory. Results indicate that proper leader language use can substantially improve the critical organizational outcome of worker retention. As such, this study identifies potential new paths for requisite leader communication research, training, and development.     

Reliability of Summed Item Scores Using Structural Equation Modeling: An Alternative to Coefficient Alpha

A method is presented for estimating reliability using structural equation modeling (SEM) that allows for nonlinearity between factors and item scores. Assuming the focus is on consistency of summed item scores, this method for estimating reliability is preferred to those based on linear SEM models and to the most commonly reported estimate of reliability, coefficient alpha.     

Doubly-Latent Models of School Contextual Effects: Integrating Multilevel and Structural Equation Approaches to Control Measurement and Sampling Error

This article is a methodological-substantive synergy. Methodologically, we demonstrate latent-variable contextual models that integrate structural equation models (with multiple indicators) and multilevel models. These models simultaneously control for and unconfound measurement error due to sampling of items at the individual (L1) and group (L2) levels and sampling error due the sampling of persons in the aggregation of L1 characteristics to form L2 constructs. We consider a set of models that are latent or manifest in relation to sampling items (measurement error) and sampling of persons (sampling error) and discuss when different models might be most useful. We demonstrate the flexibility of these 4 core models by extending them to include random slopes, latent (single-level or cross-level) interactions, and latent quadratic effects.

Substantively we use these models to test the big-fish-little-pond effect (BFLPE), showing that individual student levels of academic self-concept (L1-ASC) are positively associated with individual level achievement (L1-ACH) and negatively associated with school-average achievement (L2-ACH)—a finding with important policy implications for the way schools are structured. Extending tests of the BFLPE in new directions, we show that the nonlinear effects of the L1-ACH (a latent quadratic effect) and the interaction between gender and L1-ACH (an L1 × L1 latent interaction) are not significant. Although random-slope models show no significant school-to-school variation in relations between L1-ACH and L1-ASC, the negative effects of L2-ACH (the BFLPE) do vary somewhat with individual L1-ACH.

We conclude with implications for diverse applications of the set of latent contextual models, including recommendations about their implementation, effect size estimates (and confidence intervals) appropriate to multilevel models, and directions for further research in contextual effect analysis.     

Assessing Quality of Life in an Obesity Observational Study: a Structural Equation Modeling Approach

Applied Research in Quality of Life - Quality of life (QOL) of a patient is usually computed as the (weighted) sum of items and analysed by means of multiple regressions to evaluate its...     

病理性互联网使用与情绪、认知需要的模型构建

通过对1030名大学生进行问卷调查.对大学生的情绪、认知需要与网络成瘾的关系进行了探讨.结果发现:消极情绪对网络成瘾有显著而直接的正向作用.消极情绪对网络成瘾具有最强的预测力;认知需要对网络成瘾有显著而直接的负向作用;消极情绪和积极情绪均可通过认知需要间接地影响网络成瘾;男女生的情绪、认知需要对网络成瘾的影响存在差异.     

Multilevel Factor Analysis and Structural Equation Modeling of Daily Diary Coping Data: Modeling Trait and State Variation

This study used multilevel modeling of daily diary data to model within-person (state) and between-person (trait) components of coping variables. This application included the introduction of multilevel factor analysis (MFA) and a comparison of the predictive ability of these trait/state factors. Daily diary data were collected on a large (n = 366) multiethnic sample over the course of 5 days. Intraclass correlation coefficient for the derived factors suggested approximately equal amounts of variability in coping usage at the state and trait levels. MFAs showed that Problem-Focused Coping and Social Support emerged as stable factors at both the within-person and between-person levels. Other factors (Minimization, Emotional Rumination, Avoidance, Distraction) were specific to the within-person or between-person levels but not both. Multilevel structural equation modeling (MSEM) showed that the prediction of daily positive and negative affect differed as a function of outcome and level of coping factor. The Discussion section focuses primarily on a conceptual and methodological understanding of modeling state and trait coping using daily diary data with MFA and MSEM to examine covariation among coping variables and predicting outcomes of interest.     

Validity of the M Test: Simulation-Design and Natural-Group Approaches

This article attempts to demonstrate the value of using a psychoanalytic theory of personality for psychological testing. This approach has more clinical utility than a solely research-based one. It recasts test data into conceptually related constructs that have internal consistency to each other and are directly relevant to psychotherapeutic treatment. Such theoretical recasting serves an organizing function, an integrative function, a clarification function, and a predictive function for the clinical inference process. Furthermore, a psychoanalytically oriented approach to testing allows for the expansion in sources of data that one considers in the testing situation. Five different sources of data emerge from the testing situation once one refocuses on theoretical constructs rather than test signs. These include test scores, test content, the patient-examiner interaction, patient behavior, and examiner countertransference.     

Online Victimization,Womanism, and Body Esteem among Young Black Women: A Structural Equation Modeling Approach

Digital media use represents a central part of young adults’ daily life, within which social interactions increasingly center on visual content. While visual content, such as representations of self, may facilitate positive social interactivity, it may also increase susceptibility to harmful social interactions, such as appearance-related online victimization. Black women’s bodies are often the target of gendered racial microaggressions and sexual victimization which can contribute to body image concerns. Still, the online victimization–body esteem link among Black women remains unexamined. This study used structural equation modeling to examine the associations between four categories of online victimization (i.e., general online victimization, online individual racial victimization, online vicarious racial victimization, online sexual victimization) and body esteem. We further examined whether womanism, an identity-based factor, moderated the relationship between online victimization and body esteem. A sample of 1,595 young Black women completed an online survey. Results showed that online sexual victimization was significantly negatively associated with body esteem and that high levels of womanism buffered the harmful impact of general online victimization on body esteem. Future research is needed to examine Black women’s and gender expansive people’s experiences with online gendered racial victimization along with other forms of online intersectional oppression.

     

Structural Equation Modeling of Spatial Presence: The Influence of Cognitive Processes and Traits

This article examines the formation process of spatial presence, which is conceived as a two-step process involving the construction of a mental model of the mediated environment, followed by the emergence of spatial presence. During both stages, cognitive processes and user traits are in effect. We present data derived from a pooled set of data of three studies using the same virtual environment. Structural equation modeling is used to confirm the proposed theoretical model. The results show that attention and the trait of visual spatial imagery are positive predictors of the mental model of the mediated environment. In the second step, the formation of spatial presence is governed by involvement, the suspension of disbelief, and the domain-specific interest, together with the mental model.     

Learning Style,Metacognition and Creativity as Predictors of the Foreign Language Achievement: A Structural Equation Modeling Approach

This current study sought to examine the role of creativity, metacognition and VAK learning style in foreign language achievement. To collect the data, a sample of 122 Iranian English as Foreign Language learners participated in this research. Participants completed Creativity Instrument by Abedi (Creat Res J 14(2):267–276, 2002), Metacognitive Awareness Inventory by Schraw and Dennison (Contemp Educ Psychol 19:460–475, 1994), and visual, aural, and kinesthetic (VAK) learning style initially designed by Chislett and Chapman (VAK Learning Styles Self-Assessment Questionnaire, 2005. http://www.businessballs.com) and modified and validated by the present study researchers using confirmatory factor analysis. Learners’ scores at the end of the term were aggregated as the measure of foreign language achievement. Its reliability was also checked and approved applying Cronbach’s α. According to the findings, all three independent variables positively and significantly predict students’ language achievement, creativity (14.63%); metacognition (29.78%); and VAK learning style (7.20%). Although each of them had a unique impact on foreign language achievement, metacognition outweighs creativity and VAK learning style as the predictor of foreign language achievement. Furthermore, there is a significant difference between genders on all the variables.