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 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 Longitudinal Validation of the Levenson Self-Report Psychopathy (LSRP) Scale in a High-Risk Dutch Community Sample

Assessment ◽  
2020 ◽  
pp. 107319112097513
Author(s):  
Sophie A. Wissenburg ◽  
Carlo Garofalo ◽  
Arjan A. J. Blokland ◽  
H. Palmen ◽  
Martin Sellbom
Keyword(s):  
Construct Validity ◽  
Factor Structure ◽  
Factor Model ◽  
Psychopathic Traits ◽  
Self Report ◽  
Longitudinal Measurement Invariance ◽  
Time Points ◽  
Longitudinal Measurement ◽  
Three Factor ◽  
Over Time

The Levenson Self-Report Psychopathy (LSRP) scale is a self-report measure that can be used to assess psychopathic traits in community samples, and recent research suggested that its three-factor model (Egocentricity, Callousness, and Antisocial) has promising psychometric properties. However, no study to date has validated the LSRP in a longitudinal framework. The present study sought to validate the LSRP scale in a longitudinal design using a sample of Dutch emerging adults ( ns = 970 and 693 at time points 1 and 2, respectively). We assessed longitudinal measurement invariance and the stability of psychopathic traits over an 18-month time period, from age 20 to age 21.6. Furthermore, we replicated and extended findings on the factor structure, reliability, and construct validity of the Dutch LSRP scale. Confirmatory factor analysis revealed that the three-factor model fit the data well. Evidence of partial longitudinal measurement invariance was observed, which means that the Dutch translation of the LSRP scale is measuring an equivalent construct (and overall latent factor structure) over time. Psychopathic traits were relatively stable over time. The three LSRP subscales showed largely acceptable levels of internal consistency at both time points and showed conceptually expected patterns of construct validity and predictive validity, with a few notable exceptions.

scholarly journals Perceived vaccine safety over time in a vaccine hesitant sample: Impact of pausing due to safety concerns

2021 ◽  
Author(s):  
Thomas Rodebaugh ◽  
Madelyn Frumkin ◽  
Rachel Garg ◽  
Laura LaGesse ◽  
Amy McQueen ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Low Income ◽  
Vaccine Safety ◽  
Vaccine Hesitancy ◽  
Current Effort ◽  
Vaccination Rates ◽  
Intensive Longitudinal Data ◽  
Perceptions Of Safety ◽  
Vaccine Availability ◽  
Over Time

In the current effort to vaccinate as many people as possible against COVID-19, it has been suggested that events such as the pause in the use of the Janssen vaccine would have a large effect on perceptions of vaccine safety. Further, as vaccination rates slow, there is concern that hesitancy may be stable and difficult to change among those still unvaccinated. We examined both of these issues in our ongoing study of low-income participants. We modeled the intensive longitudinal data provided by 53 individuals. We found the negative, not statistically significant effect of the Janssen pause would be overwhelmed within weeks by the statistically significant increasing perceptions of safety across time. We also observed strong variability in vaccine hesitancy in many participants. Frequent reminders about vaccine availability might catch more people when they are less hesitant, helping increase vaccination rates.

scholarly journals The Strengths Use Scale: Psychometric Properties, Longitudinal Invariance and Criterion Validity

2021 ◽  
Vol 12 ◽  
Author(s):  
Llewellyn E. van Zyl ◽  
Diane Arijs ◽  
Matthew L. Cole ◽  
Aldona Gliíska-Newes ◽  
Lara C. Roll ◽  
...  
Keyword(s):  
Internal Consistency ◽  
Factor Model ◽  
Temporal Stability ◽  
Criterion Validity ◽  
Time Data ◽  
Personal Resource ◽  
Time Points ◽  
Strengths Use ◽  
Over Time ◽  
Study Engagement

Strengths use is an essential personal resource to consider when designing higher-educational programs and interventions. Strengths use is associated with positive outcomes for both the student (e.g., study engagement) and the university (e.g., academic throughput/performance). The Strengths Use Scale (SUS) has become a popular psychometric instrument to measure strengths use in educational settings, yet its use has been subjected to limited psychometric scrutiny outside of the U.S. Further, its longitudinal stability has not yet been established. Given the wide use of this instrument, the goals of this study were to investigate (a) longitudinal factorial validity and the internal consistency of the scale, (b) its equivalence over time, and (c) criterion validity through its relationship with study engagement over time. Data were gathered at two-time points, 3 months apart, from a sample of students in the Netherlands (n = 360). Longitudinal confirmatory factor analyses showed support for a two-factor model for overall strengths use, comprised of Affinity for Strengths and Strengths Use Behaviors. The SUS demonstrated high levels of internal consistency at both the lower- and upper bound limits at both time points. Further, strict longitudinal measurement invariance was established, which confirmed the instrument's temporal stability. Finally, criterion validity was established through relating strengths use to study engagement at different time stamps. These findings support the use of the SUS in practice to measure strengths use and to track the effectiveness of strengths use interventions within the higher education sector.

scholarly journals Factor structure of the SDQ and longitudinal associations from pre-school to pre-teen in New Zealand

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247932
Author(s):  
John M. D. Thompson ◽  
Rebecca F. Slykerman ◽  
Clare R. Wall ◽  
Rinki Murphy ◽  
Edwin A. Mitchell ◽  
...  
Keyword(s):  
Factor Analysis ◽  
New Zealand ◽  
Factor Structure ◽  
Internal Consistency ◽  
Strengths And Difficulties Questionnaire ◽  
Self Report ◽  
Time Points ◽  
Strengths And Difficulties ◽  
Over Time

Objective The objective of this study was to assess the validity of the Strengths and Difficulties Questionnaire in a cohort of New Zealand children followed from birth to the age of eleven. The study also aimed to assess the stability of the child data in relation to behavioural outcomes during this period. Methods Children in the Auckland Birthweight Collaborative Study were assessed at approximately 3½, 7 and 11 years of age. At all time-points parents completed the parent version of the Strengths and Difficulties Questionnaire, and the children themselves completed the self-report version at 11 years of age. The validity and internal consistency were assessed using exploratory factor analysis, Cronbach’s alpha, and McDonald’s Omega. Cross tabulations and Chi-square statistics were used to determine whether Total Difficulty scores, as per accepted cut-offs, remained stable over time (between normal and abnormal/borderline categories). Results The factor structure remained relatively consistent across all three time-points, though several questions did not load as per the originally published factor analysis at the earliest age. The internal consistency of the Strengths and Difficulties Questionnaire was good at all time-points and for parent- and child-completed versions. There was low agreement in the total scores between time points. Conclusions The factor analysis shows that the Strengths and Difficulties Questionnaire has a similar factor structure, particularly in older ages, to that previously published and shows good internal consistency. At the pre-school follow up, a larger than expected proportion of children were identified with high scores, particularly in the conduct sub-scale. Children’s behaviour changes over time, with only poor to moderate agreement between those identified as abnormal or borderline over the longitudinal follow up.

A Continuous-Time Mixture Latent-State-Trait Markov Model for Experience Sampling Data

2017 ◽  
Vol 33 (4) ◽  
pp. 296-311 ◽  
Author(s):  
Claudia Crayen ◽  
Michael Eid ◽  
Tanja Lischetzke ◽  
Jeroen K. Vermunt
Keyword(s):  
Longitudinal Data ◽  
Simulation Study ◽  
Continuous Time ◽  
Latent Variables ◽  
Experience Sampling ◽  
Future Research ◽  
Time Intervals ◽  
Time Model ◽  
Intensive Longitudinal Data ◽  
Time Varying Covariates

Abstract. In psychological research, statistical models of latent state-trait (LST) theory are popular for the analysis of longitudinal data. We identify several limitations of available models when applied to intensive longitudinal data with categorical observed and latent variables and inter- and intraindividually varying time intervals. As an extension of available LST models for categorical data, we describe a general mixed continuous-time LST model that is suitable for intensive longitudinal data with unobserved heterogeneity and individually varying time intervals. This model is illustrated by an application to momentary mood data that were collected in an experience sampling study (N = 164). In addition, the results of a simulation study are reported that was conducted to find out (a) the minimal data requirements with respect to sample size and number of occasions, and (b) how strong the bias is if the continuous-time structure is ignored. The empirical application revealed two classes for which the transition pattern and effects of time-varying covariates differ. In the simulation study, only small differences between the continuous-time model and its discrete-time counterpart emerged. Sample sizes N = 100 and larger in combination with six or more occasions of measurement tended to produce reliable estimation results. Implications of the models for future research are discussed.

Emotional Variability Predicts Tiredness in Daily Life

2016 ◽  
Vol 37 (3) ◽  
pp. 181-193 ◽  
Author(s):  
Aire Mill ◽  
Anu Realo ◽  
Jüri Allik
Keyword(s):  
Sampling Method ◽  
Daily Life ◽  
Emotional Experience ◽  
Experience Sampling ◽  
Experience Sampling Method ◽  
Building Blocks ◽  
Intraindividual Variability ◽  
Unique Role ◽  
Over Time

Abstract. Intraindividual variability, along with the more frequently studied between-person variability, has been argued to be one of the basic building blocks of emotional experience. The aim of the current study is to examine whether intraindividual variability in affect predicts tiredness in daily life. Intraindividual variability in affect was studied with the experience sampling method in a group of 110 participants (aged between 19 and 84 years) during 14 consecutive days on seven randomly determined occasions per day. The results suggest that affect variability is a stable construct over time and situations. Our findings also demonstrate that intraindividual variability in affect has a unique role in predicting increased levels of tiredness at the momentary level as well at the level of individuals.

Factorial Structure of the Therapist Adherence Measure-Revised (TAM-R) Within Multisystemic Therapy

2020 ◽  
Vol 36 (2) ◽  
pp. 427-431
Author(s):  
Aurelie M. C. Lange ◽  
Marc J. M. H. Delsing ◽  
Ron H. J. Scholte ◽  
Rachel E. A. van der Rijken
Keyword(s):  
Factor Analysis ◽  
Factor Structure ◽  
Factor Model ◽  
Multisystemic Therapy ◽  
Model Fit ◽  
Validity And Reliability ◽  
Adherence Measure ◽  
Therapist Adherence ◽  
Confirmatory Factor ◽  
The Us

Abstract. The Therapist Adherence Measure (TAM-R) is a central assessment within the quality-assurance system of Multisystemic Therapy (MST). Studies into the validity and reliability of the TAM in the US have found varying numbers of latent factors. The current study aimed to reexamine its factor structure using two independent samples of families participating in MST in the Netherlands. The factor structure was explored using an Exploratory Factor Analysis (EFA) in Sample 1 ( N = 580). This resulted in a two-factor solution. The factors were labeled “therapist adherence” and “client–therapist alliance.” Four cross-loading items were dropped. Reliability of the resulting factors was good. This two-factor model showed good model fit in a subsequent Confirmatory Factor Analysis (CFA) in Sample 2 ( N = 723). The current finding of an alliance component corroborates previous studies and fits with the focus of the MST treatment model on creating engagement.

Measurement Invariance of the Serbian Version of the Satisfaction With Life Scale Across Age, Gender, and Time

2019 ◽  
Vol 35 (4) ◽  
pp. 555-563 ◽  
Author(s):  
Veljko Jovanović
Keyword(s):  
Life Satisfaction ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Satisfaction With Life ◽  
Satisfaction With Life Scale ◽  
Confirmatory Factor ◽  
Life Scale ◽  
Serbian Version ◽  
Age Range ◽  
Over Time

Abstract. The present research aimed at examining measurement invariance of the Serbian version of the Satisfaction with Life Scale (SWLS) across age, gender, and time. A total sample in Study 1 consisted of 2,595 participants from Serbia, with a mean age of 23.79 years (age range: 14–55 years). The final sample in Study 2 included 333 Serbian undergraduate students ( Mage = 20.81; age range: 20–27 years), who completed the SWLS over periods of 6 and 18 months after the initial assessment. Confirmatory factor analysis (CFA) supported the modified unidimensional model of the SWLS, with correlated residuals of items 4 and 5 tapping past satisfaction. The results of the multigroup confirmatory factor analysis supported the full scalar invariance across gender and over time and partial scalar invariance across age. Latent mean comparisons revealed that women reported higher life satisfaction than men. Additionally, adolescents reported higher life satisfaction than students and adults, with adults showing the lowest life satisfaction. Our findings indicate that the SWLS allows meaningful comparisons in life satisfaction across age, gender, and over time.

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