scholarly journals What to do Without a Control Group: You have to go latent, but not all latents are equal

2021 ◽  
Author(s):  
John Protzko ◽  
Jan te Nijenhuis ◽  
Khaled Ziada ◽  
Hanaa Abdelazim Mohamed Metwaly ◽  
salaheldin Bakhiet
Keyword(s):  
Measurement Invariance ◽  
Latent Variable ◽  
Structural Equation ◽  
Change Score ◽  
Equation Modeling ◽  
Control Group ◽  
Longitudinal Structural Equation Modeling ◽  
Latent Ability ◽  
Latent Change Score ◽  
The One

The One-Group Pretest-Posttest Design, where the same group of people is measured before and after some event, can be fraught with statistical problems and issues with causal inference. Still, these designs are common from political science to developmental neuropsychology to economics. In cases with cognitive data, it has long been known that a second test, with no treatment or an ineffective manipulation between testings, leads to increased scores at time 2 without an increase in the underlying latent ability. We investigate several analytic approaches involving both manifest and latent variable modeling to see which methods are able to accurately model manifest score changes with no latent change. Using data from 600 schoolchildren given an intelligence test twice, with no intervention between, we show using manifest test scores, either directly or through univariate latent change score analysis, falsely leads one to believe an underlying increase has occurred. Latent change score models on latent data also show a spurious significant effect on the underlying latent ability. Multigroup Confirmatory Factor Analysis only shows the correct answer when measurement invariance is tested, imposed (if viable), and the means of both time points are tested constricting time 2 to zero. Longitudinal structural equation modeling with measurement invariance correctly shows no change at the latent level when measurement invariance is tested, imposed, and model fit tested. When dealing with the One-Group Pretest-Posttest Design, analyses must occur at the latent level, measurement invariance tested, and change parameters explicitly tested. Otherwise, one may see change where none exists.

scholarly journals Longitudinal measurement invariance in urbanization index of Chinese communities across 2000 and 2015: a Bayesian approximate measurement invariance approach

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ted C. T. Fong ◽  
Rainbow T. H. Ho
Keyword(s):  
Measurement Invariance ◽  
Factor Structure ◽  
Structural Equation ◽  
Equation Modeling ◽  
Nutrition Survey ◽  
Longitudinal Measurement Invariance ◽  
Longitudinal Measurement ◽  
The One ◽  
Bayesian Structural Equation Modeling ◽  
Urbanicity Scale

Abstract Background The Urbanicity Scale was developed based on the China Health and Nutrition Survey (CHNS) to measure the urbanization index of communities according to 12 components. The present study was designed to systematically investigate the factorial validity, reliability, and longitudinal measurement invariance (LMI) of the Urbanicity Scale. Methods Six waves of CHNS data from 2000 to 2015 were adopted. The factor structure and reliability of the Urbanicity Scale for 301 communities were examined using Bayesian exploratory factor analysis. Metric and scalar LMIs were evaluated using both the conventional exact and a novel approximate LMI approach via Bayesian structural equation modeling across various timeframes. Results The findings verified the one-factor structure for the Urbanicity Scale, with adequate reliability. LMI was established for the Urbanicity Scale only over a shorter timeframe from 2006 to 2009 but not over a longer timeframe from 2000 to 2015. Partial LMI was found in the factor loadings and item intercepts for the Urbanicity Scale over the 2004 to 2011 period. Conclusion Interpretation of the temporal change in urbanicity was supported only for a shorter (2006 to 2009) but not a longer timeframe (2000 to 2015). Adjustments addressing the partial non-invariance of the measurement parameters are needed for the analysis of temporal changes in urbanicity between 2004 and 2011.

The benefits of latent variable modeling to develop norms for a translated version of a standardized scale

2016 ◽  
Vol 41 (6) ◽  
pp. 743-750 ◽  
Author(s):  
Hyojeong Seo ◽  
Leslie A. Shaw ◽  
Karrie A. Shogren ◽  
Kyle M. Lang ◽  
Todd D. Little
Keyword(s):  
Measurement Invariance ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
Model Parameters ◽  
Confirmatory Factor Analyses ◽  
Standardization Sample ◽  
Multiple Group ◽  
The Us ◽  
Latent Means

This article demonstrates the use of structural equation modeling to develop norms for a translated version of a standardized scale, the Supports Intensity Scale – Children’s Version (SIS-C). The latent variable norming method proposed is useful when the standardization sample for a translated version is relatively small to derive norms independently but the original standardization sample is larger and more robust. Specifically, we leveraged a large, representative US standardization sample ( n = 4,015) to add power and stability to a smaller Spanish ( n = 405) standardization sample. Using a series of multiple-group mean and covariance structures confirmatory factor analyses using effects-coded scaling constraints, measurement invariance was tested across (a) Spanish only and (b) both US and Spanish age bands (5–6, 7–8, 9–10, 11–12, 13–14, and 15–16). After establishing measurement invariance across the US and Spain, tests for latent means and variance differences within age-bands were only performed for Spanish data; the latent means and variances in the US sample were freely estimated. The study findings suggest that the information in the US data stabilized the overall model parameters, and the inclusion of the US sample did not influence on the norms of the SIS-C Spanish Translation.

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.

Neuroimaging of Individual Differences: A Latent Variable Modeling Perspective

2018 ◽  
Author(s):  
Shelly Renee Cooper ◽  
Joshua James Jackson ◽  
Deanna Barch ◽  
Todd Samuel Braver
Keyword(s):  
Individual Differences ◽  
Individual Difference ◽  
Latent Variable ◽  
Structural Equation ◽  
Behavioral Measure ◽  
Equation Modeling ◽  
Latent Variable Modeling ◽  
Psychometric Theory ◽  
Human Connectome Project ◽  
Neuroimaging Data

Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.

scholarly journals Postpartum depression symptoms in survey-based research: a structural equation analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Che Wan Jasimah Bt Wan Mohamed Radzi ◽  
Hashem Salarzadeh Jenatabadi ◽  
Nadia Samsudin
Keyword(s):  
Postpartum Depression ◽  
Latent Variable ◽  
Structural Equation ◽  
Depression Scale ◽  
Public Health Problem ◽  
Equation Modeling ◽  
Depression Symptoms ◽  
Postpartum Women ◽  
Unhealthy Food ◽  
High Level

Abstract Background Since the last decade, postpartum depression (PPD) has been recognized as a significant public health problem, and several factors have been linked to PPD. Mothers at risk are rarely undetected and underdiagnosed. Our study aims to determine the factors leading to symptoms of depression using Structural Equation Modeling (SEM) analysis. In this research, we introduced a new framework for postpartum depression modeling for women. Methods We structured the model of this research to take into consideration the Malaysian culture in particular. A total of 387 postpartum women have completed the questionnaire. The symptoms of postpartum depression were examined using the Edinburgh Postnatal Depression Scale (EPDS), and they act as a dependent variable in this research model. Results Four hundred fifty mothers were invited to participate in this research. 86% of the total distributed questionnaire received feedback. The majority of 79.6% of respondents were having depression symptoms. The highest coefficients of factor loading analysis obtained in every latent variable indicator were income (β = 0.77), screen time (β = 0.83), chips (β = 0.85), and anxiety (β = 0.88). Lifestyle, unhealthy food, and BMI variables were directly affected by the dependent variable. Based on the output, respondents with a high level of depression symptoms tended to consume more unhealthy food and had a high level of body mass indexes (BMI). The highest significant impact on depression level among postpartum women was unhealthy food consumption. Based on our model, the findings indicated that 76% of the variances stemmed from a variety of factors: socio-demographics, lifestyle, healthy food, unhealthy food, and BMI. The strength of the exogenous and endogenous variables in this research framework is strong. Conclusion The prevalence of postpartum women with depression symptoms in this study is considerably high. It is, therefore, imperative that postpartum women seek medical help to prevent postpartum depressive symptoms from worsening.

scholarly journals Insights Into the Correspondence Between Land Use Pattern and Spatial Distribution of Rail Transit Services

2021 ◽  
Author(s):  
Zhongqi Wang ◽  
Qi Han ◽  
Bauke de Vries ◽  
Li Dai
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Operations Management ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
Rail Transit ◽  
Land Use Patterns ◽  
Transit Services ◽  
The Relationship

AbstractThe identification of the relationship between land use and transport lays the foundation for integrated land use and transport planning and management. This work aims to investigate how rail transit is linked to land use. The research on the relationship between land use and rail-based transport is dominated by the impacts of rail projects on land use, without an in-depth understanding of the reverse. However, it is important to note that issues of operation management rather than new constructions deserve greater attention for regions with established rail networks. Given that there is a correspondence between land use patterns and spatial distribution of heavy railway transit (HRT) services at such regions, the study area (i.e., the Netherlands) is partitioned by the Voronoi diagram of HRT stations and the causal relationship between land use and HRT services is examined by structural equation modeling (SEM). The case study of Helmond (a Dutch city) shows the potential of the SEM model for discussing the rail station selection problem in a multiple transit station region (MTSR). Furthermore, in this study, the node place model is adapted with the derivatives of the SEM model (i.e., the latent variable scores for rail service levels and land use characteristics), which are assigned as node and place indexes respectively, to analyze and differentiate the integration of land use and HRT services at the regional level. The answer to whether and how land use affects rail transit services from this study strengthens the scientific basis for rail transit operations management. The SEM model and the modified node place model are complementary to be used as analytical and decision-making tools for rail transit-oriented regional development.

Correlated personality change across time and age

2021 ◽  
pp. 089020702110140
Author(s):  
Gabriel Olaru ◽  
Mathias Allemand
Keyword(s):  
Structural Equation ◽  
Rank Order ◽  
Personality Development ◽  
The United States ◽  
Change Score ◽  
Personality Change ◽  
Equation Modeling ◽  
Model Parameters ◽  
Cross Sectional ◽  
Adult Lifespan

The goal of this study was to examine differential and correlated change in personality across the adult lifespan. Studying differential and correlated change can help understand whether intraindividual trait change trajectories deviate from the norm and how these trajectories are coupled with each other. We used data from two large longitudinal panel studies from the United States that covered a total age range of 20 to 95 years on the first measurement occasion. We used correlated factor models and bivariate latent change score models to examine the rank-order stability and correlations between change across three measurement waves covering 18 years ( N = 3250) and four measurement waves covering 12 years ( N = 4145). We examined the moderation effects of continuous age on these model parameters using local structural equation modeling. The results suggest that the test–retest correlations decrease with increasing time between measurements but are unaffected by participants’ age. We found that change processes in Extraversion, Openness, Agreeableness, and Conscientiousness were strongly related, particularly in late adulthood. Correlated change patterns were highly stable across time intervals and similar to the initial cross-sectional Big Five correlations. We discuss potential mechanisms and implications for personality development research.

scholarly journals Validity and Internal Consistency of the Preschool-FLAT, a New Tool for the Assessment of Food Literacy in Young Children from the Training-To-Health Project

2020 ◽  
Vol 17 (8) ◽  
pp. 2759
Author(s):  
Garden Tabacchi ◽  
Giuseppe Battaglia ◽  
Giuseppe Messina ◽  
Antonio Paoli ◽  
Antonio Palma ◽  
...  
Keyword(s):  
Internal Consistency ◽  
Content Validity ◽  
Structural Equation ◽  
Discriminant Validity ◽  
Assessment Tool ◽  
Intervention Group ◽  
Equation Modeling ◽  
Control Group ◽  
Food Literacy ◽  
Health Project

Background: The importance of assessing “food literacy” since youth has been highlighted and, to this purpose, valid and consistent instruments are needed. This study aimed to assess the validity and internal consistency of the preschool-FLAT (Food Literacy Assessment Tool). Methods. 505 children from 21 kindergartens, recruited within the Training-to-Health Project in Palermo (Italy), underwent oral sessions and activities on food-related aspects. Their knowledge/skills were recorded in the preschool-FLAT. The following scale measures were assessed: Content validity; internal consistency (Chronbach’s alpha coefficients); construct validity (Structural Equation Modeling—SEM); discriminant validity (intervention subgroup of 100 children vs. control group of 27 children). Results. Acceptable content validity of a 16-items scale and overall adequate internal consistency were revealed: Content validity index (CVI) 0.94, content validity ratio (CVR) 0.88, Chronbach’s alpha 0.76. The SEM revealed a 4-factor model fitting the data well (comparative fit index 0.939, root mean square error of approximation 0.033). Discriminant validity was good (intervention group scoring higher than control, p < 0.001, unpaired Student’s t-test). Conclusion. The preschool-FLAT revealed good psychometric properties, adequate validity and internal consistency. This is the only instrument in the literature specifically targeted to 3–6 years old children that could be effectively used to assess food literacy.

Attributional Style as a Predictor of Hopelessness Depression

2006 ◽  
Vol 20 (4) ◽  
pp. 447-458 ◽  
Author(s):  
Edward D. Sturman ◽  
Myriam Mongrain ◽  
Paul M. Kohn
Keyword(s):  
Attributional Style ◽  
Latent Variable ◽  
Structural Equation ◽  
Depression Scale ◽  
Epidemiological Studies ◽  
Psychomotor Retardation ◽  
Equation Modeling ◽  
Depression Symptoms ◽  
Negative Events ◽  
Hopelessness Depression

Stable and global attributions for negative events were tested as predictors of hopelessness depression symptoms, obtained from a diagnostic interview for a past depressive episode in a sample of 102 graduate students. All participants were administered the Structured Clinical Interview for DSM–IV, Center for Epidemiological Studies Depression Scale, Personal Style Inventory, and a modified version of the Extended Attributional Style Questionnaire. A stable and global attributional style for negative events was significantly associated with a composite of hopelessness depression symptoms. A regression analysis revealed that attributional style significantly postdicted hopelessness depression symptoms when controlling for both sociotropy and autonomy. Structural equation modeling supported a model in which stable and global attributions predicted a latent variable, which we refer to as a motivational deficit, involving psychomotor retardation and fatigue as indicators. Therefore, this study obtained some support for the hopelessness model and highlights the vulnerability posed by attributional style ( Abramson, Metalsky, & Alloy, 1989 ).

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