scholarly journals Measurement in Intensive Longitudinal Data

2021 ◽  
Author(s):  
Daniel McNeish
Keyword(s):  
Longitudinal Data ◽  
Structural Equation ◽  
Self Regulation ◽  
Measurement Model ◽  
Equation Modeling ◽  
Longitudinal Methods ◽  
Intensive Longitudinal Data ◽  
Measurement Models ◽  
Technological Advances ◽  
Intensive Longitudinal Methods

Technological advances have increased the prevalence of intensive longitudinal data as well as statistical techniques appropriate for these data, such as dynamic structural equation modeling (DSEM). Intensive longitudinal designs often investigate constructs related to affect or mood and do so with multiple item scales. However, applications of intensive longitudinal methods often rely on simple sums or averages of the administered items rather than considering a proper measurement model. This paper demonstrates how to incorporate measurement models into DSEM to (1) provide more rigorous measurement of constructs used in intensive longitudinal studies and (2) assess whether scales are invariant across time and across people, which is not possible when item responses are summed or averaged. We provide an example from an ecological momentary assessment study on self-regulation in adults with binge eating disorder and walkthrough how to fit the model in Mplus and how to interpret the results.

Ant Colony Optimization and Local Weighted Structural Equation Modeling. A Tutorial on Novel Item and Person Sampling Procedures for Personality Research

2019 ◽  
Vol 33 (3) ◽  
pp. 400-419 ◽  
Author(s):  
Gabriel Olaru ◽  
Ulrich Schroeders ◽  
Johanna Hartung ◽  
Oliver Wilhelm
Keyword(s):  
Structural Equation Modeling ◽  
Ant Colony Optimization ◽  
Structural Equation ◽  
Personality Development ◽  
Measurement Model ◽  
Ant Colony ◽  
Equation Modeling ◽  
Moderator Variable ◽  
Measurement Models ◽  
Sampling Procedures

Measurement in personality development faces many psychometric problems. First, theory–based measurement models do not fit the empirical data in terms of traditional confirmatory factor analysis. Second, measurement invariance across age, which is necessary for a meaningful interpretation of age–associated personality differences, is rarely accomplished. Finally, continuous moderator variables, such as age, are often artificially categorized. This categorization leads to bias when interpreting differences in personality across age. In this tutorial, we introduce methods to remedy these problems. We illustrate how Ant Colony Optimization can be used to sample indicators that meet prespecified demands such as model fit. Further, we use Local Structural Equation Modeling to resample and weight subjects to study differences in the measurement model across age as a continuous moderator variable. We also provide a detailed illustration for both tools with the Neuroticism scale of the openly available International Personality Item Pool – NEO inventory using data from the UK sample ( N = 15 827). Combined, both tools can remedy persistent problems in research on personality and its development. In addition to a step–by–step illustration, we provide commented syntax for both tools. © 2019 European Association of Personality Psychology

scholarly journals Approaches for Modeling of Intensive Longitudinal Data to Understand Cognitive Aging

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 565-565
Author(s):  
Karra Harrington ◽  
Nelson Roque ◽  
Jacqueline Mogle
Keyword(s):  
New York ◽  
Longitudinal Data ◽  
Cognitive Performance ◽  
Cognitive Aging ◽  
Exponential Model ◽  
Longitudinal Methods ◽  
Intensive Longitudinal Data ◽  
Valid Measurement ◽  
Age Related ◽  
Technological Advances

Abstract Understanding age-related change in cognition and identification of pathological changes requires sensitive and valid measurement of cognitive performance across time. Technological advances, such as ambulatory assessment of cognition using smartphones, have enabled intensive longitudinal methods where data is collected with many measurements over time. Our research group has developed novel ambulatory assessments that provide reliable, sensitive, and ecologically valid measurement of cognition across multiple timescales; from momentary changes to change across years. This symposium will present a spectrum of approaches to analysis of intensive longitudinal data that can inform models of cognitive aging. All three presentations will draw on data from measurement burst studies that apply our ambulatory cognitive assessment methods in community-based samples (i.e., systematically recruited in the Bronx, New York). For each measurement burst, participants undergo assessment consisting of brief surveys and cognitive tests via smartphone, up to 7 times per day across 14 days. Oravecz et al. will discuss the application of a Bayesian multilevel implementation of the double exponential model to account for retest effects while quantifying change in peak cognitive performance across time. Kang et al., will demonstrate a growth curve modeling approach for assessing the effects of between-person variables (i.e., loneliness) on change in cognition across measurement bursts. Harrington et al., will demonstrate a model-based cluster analysis approach, leveraging ambulatory assessments of subjective and objective cognitive function to unpack latent groups as a function of age and loneliness. Measurement, Statistics, and Research Design Interest Group Sponsored Symposium.

Multilevel Structural Equation Modeling for Intensive Longitudinal Data: A Practical Guide for Personality Researchers

2019 ◽  
Author(s):  
Gentiana Sadikaj ◽  
Aidan G.C. Wright ◽  
David Dunkley ◽  
David Zuroff ◽  
D.S. Moskowitz
Keyword(s):  
Structural Equation Modeling ◽  
Longitudinal Data ◽  
Latent Variable ◽  
Structural Equation ◽  
Longitudinal Research ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Intensive Longitudinal Data ◽  
Mixed Effects Modeling ◽  
Multilevel Structural Equation

Intensive longitudinal research designs are increasingly used to study personality processes. The resulting data can be highly informative in ways that other data cannot, but these data also pose statistical challenges. Most often a multilevel or mixed effects modeling approach is adopted which is appropriate but may not be optimal. Surprisingly little attention is given to reliability of measurement, and the models often lack adequate complexity to test theoretical questions of interest. These limitations can be addressed with multilevel structural equation modeling (MSEM), which weds the ability to deal with nested data structures with the strengths of structural equation modeling (e.g., latent variable models, multiple outcomes and mediators). This article provides a gentle introduction to MSEM for personality researchers. Following an initial review of the relevant challenges facing researchers interested in studying personality using intensive longitudinal data, basic issues in MSEM are summarized, and a series of example models are presented. The online supplementary material provides Mplus syntax for the models presented.

scholarly journals Using Structural Equation Modeling to Study Traits and States in Intensive Longitudinal Data

2020 ◽  
Author(s):  
Sebastian Castro-Alvarez ◽  
Jorge Tendeiro ◽  
Rob Meijer ◽  
Laura Francina Bringmann
Keyword(s):  
Longitudinal Data ◽  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Models ◽  
Structural Equation Models ◽  
Longitudinal Research ◽  
Equation Modeling ◽  
Intensive Longitudinal Data ◽  
The Common ◽  
The Stability

Traditionally, researchers have used time series and multilevel models to analyze intensive longitudinal data. However, these models do not directly address traits and states which conceptualize the stability and variability implicit in longitudinal research, and they do not explicitly take into account measurement error. An alternative to overcome these drawbacks is to consider structural equation models (state-trait SEMs) for longitudinal data that represent traits and states as latent variables. Most of these models are encompassed in the Latent State-Trait (LST) theory. These state-trait SEMs can be problematic when the number of measurement occasions increases. As they require the data to be in wide format, these models quickly become overparameterized and lead to non-convergence issues. For these reasons, multilevel versions of state-trait SEMs have been proposed, which require the data in long format. To study how suitable state-trait SEMs are for intensive longitudinal data, we carried out a simulation study. We compared the traditional single level to the multilevel version of three state-trait SEMs. The selected models were the multistate-singletrait (MSST) model, the common and unique trait-state (CUTS) model, and the trait-state-occasion (TSO) model. Furthermore, we also included an empirical application. Our results indicated that the TSO model performed best in both the simulated and the empirical data. To conclude, we highlight the usefulness of state-trait SEMs to study the psychometric properties of the questionnaires used in intensive longitudinal data. Yet, these models still have multiple limitations, some of which might be overcome by extending them to more general frameworks.

A Primer on Two-Level Dynamic Structural Equation Models for Intensive Longitudinal Data

2018 ◽  
Author(s):  
Daniel McNeish
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Longitudinal Data ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Intensive Longitudinal Data ◽  
Series Analysis ◽  
Technological Advances ◽  
Basic Ideas ◽  
Result Interpretation

Technological advances have led to an increase in intensive longitudinal data and the statistical literature on modeling such data is rapidly expanding, as are software capabilities. Common methods in this area are related to time-series analysis, a framework that historically has received little exposure in psychology. There is a scarcity of psychology-based resources introducing the basic ideas of time-series analysis, especially for datasets featuring multiple people. We begin with basics of N=1 time-series analysis and build up to complex dynamic structural equation models available in the newest release of Mplus. The goal is to provide readers with a basic conceptual understanding of common models, template code, and result interpretation. We provide short descriptions of some advanced issues, but our main priority is to supply readers with a solid knowledge base so that the more advanced literature on the topic is more readily digestible to a larger group of researchers

Age and gender differences in socially aversive (“dark”) personality traits

2021 ◽  
pp. 089020702098843
Author(s):  
Johanna Hartung ◽  
Martina Bader ◽  
Morten Moshagen ◽  
Oliver Wilhelm
Keyword(s):  
Personality Traits ◽  
Structural Equation ◽  
Age Groups ◽  
Dark Triad ◽  
Equation Modeling ◽  
Model Parameters ◽  
Age And Gender ◽  
Measurement Models ◽  
And Gender ◽  
Latent Means

The strong overlap of personality traits discussed under the label of “dark personality” (e.g., psychopathy, spitefulness, moral disengagement) endorses a common framework for socially aversive traits over and beyond the dark triad. Despite the rapidly growing research on socially aversive traits, there is a lack of studies addressing age-associated differences in these traits. In the present study ( N = 12,501), we investigated the structure of the D Factor of Personality across age and gender using local structural equation modeling, thereby expressing the model parameters as a quasi-continuous, nonparametric function of age. Specifically, we evaluated loadings, reliabilities, factor (co-)variances, and means across 35 locally weighted age groups (from 20 to 54 years), separately for females and males. Results indicated that measurement models were highly stable, thereby supporting the conceptualization of the D factor independent of age and gender. Men exhibited uniformly higher latent means than females and all latent means decreased with increasing age. Overall, D and its themes were invariant across age and gender. Therefore, future studies can meaningfully pursue causes of mean differences across age and between genders.

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