scholarly journals How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa

2021 ◽  
Author(s):  
Leonie V. D. E. Vogelsmeier ◽  
Jeroen K. Vermunt ◽  
Kim De Roover
Keyword(s):  
Longitudinal Data ◽  
Open Source Software ◽  
Software Package ◽  
R Package ◽  
Measurement Model ◽  
Response Styles ◽  
Psychological Constructs ◽  
Intensive Longitudinal Data ◽  
Time Points ◽  
User Friendly

Intensive longitudinal data (ILD) have become popular for studying within-person dynamics in psychological constructs (or between-person differences therein). Prior to investigating what the dynamics look like, it is important to examine whether the measurement model (MM) is the same across subjects and time and, thus, whether the measured constructs have the same meaning. If the MM differs (e.g., because of changes in item interpretation or response styles), observations cannot be validly compared. Exploring differences in the MM for ILD can be done with latent Markov factor analysis (LMFA), which classifies observations based on the underlying MM (for many subjects and time-points simultaneously) and thus shows which observations are comparable. However, the complexity of the method or the fact that no open-source software for LMFA existed until now may have hindered researchers from applying the method in practice. In this article, we introduce the new user-friendly software package lmfa, which allows researchers to perform the analysis in the freely available software R. We provide a step-by-step tutorial for the lmfa package so that researchers can easily investigate MM differences in their own ILD.

scholarly journals Latent Markov Latent Trait Analysis for Exploring Measurement Model Changes in Intensive Longitudinal Data

2020 ◽  
pp. 016327872097676
Author(s):  
Leonie V. D. E. Vogelsmeier ◽  
Jeroen K. Vermunt ◽  
Loes Keijsers ◽  
Kim De Roover
Keyword(s):  
Longitudinal Data ◽  
Measurement Invariance ◽  
Ordinal Data ◽  
Latent Trait ◽  
Well Being ◽  
Measurement Model ◽  
Social Contexts ◽  
Psychological Constructs ◽  
Intensive Longitudinal Data ◽  
Trait Analysis

Drawing inferences about dynamics of psychological constructs from intensive longitudinal data requires the measurement model (MM)—indicating how items relate to constructs—to be invariant across subjects and time-points. When assessing subjects in their daily life, however, there may be multiple MMs, for instance, because subjects differ in their item interpretation or because the response style of (some) subjects changes over time. The recently proposed “latent Markov factor analysis” (LMFA) evaluates (violations of) measurement invariance by classifying observations into latent “states” according to the MM underlying these observations such that MMs differ between states but are invariant within one state. However, LMFA is limited to normally distributed continuous data and estimates may be inaccurate when applying the method to ordinal data (e.g., from Likert items) with skewed responses or few response categories. To enable researchers and health professionals with ordinal data to evaluate measurement invariance, we present “latent Markov latent trait analysis” (LMLTA), which builds upon LMFA but treats responses as ordinal. Our application shows differences in MMs of adolescents’ affective well-being in different social contexts, highlighting the importance of studying measurement invariance for drawing accurate inferences for psychological science and practice and for further understanding dynamics of psychological constructs.

scholarly journals Latent Markov Factor Analysis: A Mixture Modeling Approach for Evaluating Within- and Between-Person Measurement Model Differences in Intensive Longitudinal Data

2022 ◽  
Author(s):  
Leonie V. D. E. Vogelsmeier
Keyword(s):  
Factor Analysis ◽  
Longitudinal Data ◽  
Factor Model ◽  
Experience Sampling ◽  
Response Styles ◽  
New Approach ◽  
Intensive Longitudinal Data ◽  
Time Points ◽  
Depressive Episodes ◽  
Over Time

SUMMARY DOCTORAL DISSERTATION: Experience sampling methodology, in which participants are repeatedly questioned via smartphone apps, is popular for studying psychological constructs or “factors” (e.g., well-being or depression) within persons over time. The validity of such studies (e.g., concerning treatment decisions) may be hampered by distortions of the measurement of the relevant constructs due to response styles or item interpretations that change over time and differ across persons. In this PhD project, we developed a new approach to evaluate person- and time-point-specific distortions of the construct measurements, taking into account the specific characteristics of (time-intensive) longitudinal data inherent to experience sampling studies. Our new approach, latent Markov factor analysis, extends mixture factor analysis and clusters time-points within persons according to their factor model. The factor model describes how well items measure the constructs. With the new approach, researchers can examine how many and which factor models underlie the data, for which persons and time-points they apply, and thus which observations are validly comparable. Such insights can also be interesting in their own right. In personalized healthcare, for example, detecting changes in response styles is critical for accurate decisions about treatment allocation over time, as response styles may be related to the occurrence of depressive episodes.

scholarly journals Mixed-Effects Trait-State-Occasion Model: Studying the Psychometric Properties and the Person-Situation Interactions of Psychological Dynamics

2021 ◽  
Author(s):  
Sebastian Castro-Alvarez ◽  
Jorge Tendeiro ◽  
Peter de Jonge ◽  
Rob Meijer ◽  
Laura Francina Bringmann
Keyword(s):  
Longitudinal Data ◽  
Positive Affect ◽  
Psychometric Properties ◽  
Measurement Model ◽  
Mixed Effects ◽  
Psychological Constructs ◽  
Intensive Longitudinal Data ◽  
Trait Theory ◽  
Psychological Dynamics ◽  
Carry Over

The trait-state-occasion model (TSO) is a popular model within the latent state-trait theory (LST). The TSO allows distinguishing the trait and the state components of the psychological constructs measured in longitudinal data, while also taking into account the carry-over effects between consecutive measurements. In the present study, we extend a multilevel version of the TSO model to allow for the combination of fixed and random situations, namely the mixed-effects TSO (ME-TSO). Hence, the ME-TSO model is a measurement model suitable to analyze intensive longitudinal data that allows studying the psychometric properties of the indicators per individual, the heterogeneity of psychological dynamics, and the person-situation interaction effects. We showcase how to use the model by analyzing the items of positive affect activation of the crowdsourcing study HowNutsAreTheDutch (HoeGekisNL).

scholarly journals P.R.E.S.S. — AN R-PACKAGE FOR EXPLORING RESIDUAL-LEVEL PROTEIN STRUCTURAL STATISTICS

2012 ◽  
Vol 10 (03) ◽  
pp. 1242007 ◽  
Author(s):  
YUANYUAN HUANG ◽  
STEPHEN BONETT ◽  
ANDRZEJ KLOCZKOWSKI ◽  
ROBERT JERNIGAN ◽  
ZHIJUN WU
Keyword(s):  
Structural Properties ◽  
Open Source Software ◽  
Protein Structures ◽  
R Package ◽  
Residue Level ◽  
Large Set ◽  
Torsion Angles ◽  
Residual Level ◽  
Open Source Software Package ◽  
User Friendly

P.R.E.S.S. is an R-package developed to allow researchers to get access to and manipulate a large set of statistical data on protein residue-level structural properties such as residue-level virtual bond lengths, virtual bond angles, and virtual torsion angles. A large set of high-resolution protein structures is downloaded and surveyed. Their residue-level structural properties are calculated and documented. The statistical distributions and correlations of these properties can be queried and displayed. Tools are also provided for modeling and analyzing a given structure in terms of its residue-level structural properties. In particular, new tools for computing residue-level statistical potentials and displaying residue-level Ramachandran-like plots are developed for structural analysis and refinement. P.R.E.S.S. has been released in R as an open source software package, with a user-friendly GUI, accessible and executable by a public user in any R environment. P.R.E.S.S. can also be downloaded directly at http://www.math.iastate.edu/press/ .

scholarly journals cognac: rapid generation of concatenated gene alignments for phylogenetic inference from large whole genome sequencing datasets

2020 ◽  
Author(s):  
Ryan D. Crawford ◽  
Evan S. Snitkin
Keyword(s):  
Phylogenetic Analysis ◽  
Genome Sequencing ◽  
Software Package ◽  
Genomic Data ◽  
Phylogenetic Inference ◽  
R Package ◽  
Core Gene ◽  
Whole Genome ◽  
Rapid Generation ◽  
User Friendly

AbstractThe quantity of genomic data is expanding at an increasing rate. Tools for phylogenetic analysis which scale to the quantity of available data are required. We present cognac, a user-friendly software package to rapidly generate concatenated gene alignments for phylogenetic analysis. We applied this tool to generate core gene alignments for very large genomic datasets, including a dataset of over 11,000 genomes from the genus Escherichia containing 1,353 genes, which was constructed in less than 17 hours. We have released cognac as an R package (https://github.com/rdcrawford/cognac) with customizable parameters for adaptation to diverse applications.

scholarly journals BioRssay: An R Package for Analyses of Bioassays and Probit Graphs

2021 ◽  
Author(s):  
Piyal Karunarathne ◽  
Nicolas Pocquet ◽  
Pierrick Labbé ◽  
Pascal Milesi
Keyword(s):  
Public Health ◽  
Dose Response ◽  
Software Package ◽  
R Package ◽  
World Health ◽  
Research Areas ◽  
Exposure Response ◽  
The World ◽  
Health Organization ◽  
User Friendly

Abstract Dose-response relationships reflect the effects of a substance on organisms, and are widely used in broad research areas, from medicine and physiology, to vector control and pest management in agronomy. Furthermore, reporting on the response of organisms to stressors is an essential component of many public policies (e.g. public health, environment), and assessment of xenobiotic responses is an integral part of the World Health Organization recommendations. Building upon an R script that we previously made available, and considering its popularity, we have now developed a software package in the R environment, BioRssay, to efficiently analyze dose-response relationships. It has more user-friendly functions, more flexibility, and proposes an easy interpretation of the results. The functions in the BioRssay package are built on robust statistical analyses to compare the dose/exposure-response of various bioassays and effectively visualize them in probit-graphs.

scholarly journals modglm: An R package for testing, interpreting, and displaying interactions in generalized linear models of discrete data

2021 ◽  
Author(s):  
Connor McCabe ◽  
Max Andrew Halvorson ◽  
Kevin Michael King ◽  
Xiaolin Cao ◽  
Dale Sim Kim
Keyword(s):  
Open Source ◽  
Generalized Linear Models ◽  
Open Source Software ◽  
Interaction Effect ◽  
Software Package ◽  
Linear Models ◽  
R Package ◽  
Interaction Effects ◽  
Research Practice ◽  
Nonlinear Scale

Many researchers hope to examine interaction effects using generalized linear models (GLMs) to predict outcomes on nonlinear scales. For instance, logistic and Poisson GLMs are used to estimate associations between predictors and outcomes in nonlinear probability and count scales, respectively. However, we (McCabe et al., 2021; Halvorson et al., in press) and others (Ai & Norton, 2003; Mize, 2019; Norton, Wang, & Ai, 2004) have shown that testing and interpreting interaction effects on these scales is not straightforward. GLMs require the application of partial derivatives and/or discrete differences to compute and probe interaction effects appropriately when models are interpreted on their nonlinear scale. Currently available open-source software does not provide methods of computing these interaction effects on probability and count scales, reflecting a central limitation in applying these methods in research practice. Here, we introduce `modglm`, an R-based software package that accompanies our manuscript providing recommendations for computing interaction effects in nonlinear probability and counts (McCabe et al., 2021). This software produces the interaction effect between two variables in generalized linear models of probabilities and counts and provides additional statistics and plotting utilities for evaluating and describing this effect.

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.

scholarly journals cognac: rapid generation of concatenated gene alignments for phylogenetic inference from large, bacterial whole genome sequencing datasets

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ryan D. Crawford ◽  
Evan S. Snitkin
Keyword(s):  
Phylogenetic Analysis ◽  
Software Package ◽  
Phylogenetic Inference ◽  
R Package ◽  
Core Gene ◽  
Data Driven ◽  
Marker Genes ◽  
Data Driven Approach ◽  
Rapid Generation ◽  
User Friendly

Abstract Background The quantity of genomic data is expanding at an increasing rate. Tools for phylogenetic analysis which scale to the quantity of available data are required. To address this need, we present cognac, a user-friendly software package to rapidly generate concatenated gene alignments for phylogenetic analysis. Results We illustrate that cognac is able to rapidly identify phylogenetic marker genes using a data driven approach and efficiently generate concatenated gene alignments for very large genomic datasets. To benchmark our tool, we generated core gene alignments for eight unique genera of bacteria, including a dataset of over 11,000 genomes from the genus Escherichia producing an alignment with 1353 genes, which was constructed in less than 17 h. Conclusions We demonstrate that cognac presents an efficient method for generating concatenated gene alignments for phylogenetic analysis. We have released cognac as an R package (https://github.com/rdcrawford/cognac) with customizable parameters for adaptation to diverse applications.

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