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Using Pointwise Mutual Information for Breast Cancer Health Disparities Research With SEER-Medicare Claims. Methodology (IF 2.0) Pub Date : 2023-03-31 Brian L Egleston,Ashis Kumar Chanda,Tian Bai,Carolyn Y Fang,Richard J Bleicher,Slobodan Vucetic
Identification of procedures using International Classification of Diseases or Healthcare Common Procedure Coding System codes is challenging when conducting medical claims research. We demonstrate how Pointwise Mutual Information can be used to find associated codes. We apply the method to an investigation of racial differences in breast cancer outcomes. We used Surveillance Epidemiology and End Results
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One-way and two-way anova: Inferences about a robust, heteroscedastic measure of effect size Methodology (IF 2.0) Pub Date : 2022-03-31 Rand Wilcox
Consider a one-way or two-way ANOVA design. Typically, groups are compared based on some measure of location. The paper suggests alternative methods where measures of location are replaced by a robust measure of effect size that is based in part on a robust measure of dispersion. The measure of effect size used here does not assume that the groups have a common measure of dispersion. That is, it deals
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Bias correction for eta squared in one-way ANOVA Methodology (IF 2.0) Pub Date : 2022-03-31 Xiaofeng Steven Liu
Eta squared is a popular effect size, but contains positive bias. Bootstrapping can be used to remove the bias from eta squared. Compared to epsilon squared and omega squared, bootstrap bias correction does not make distributional assumption, and it is easy to implement. A real example and computer simulations are included to illustrate its application. The bootstrap bias-corrected eta squared shows
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The A Priori Procedure for estimating the mean in both log-normal and gamma populations and robustness for assumption violations Methodology (IF 2.0) Pub Date : 2022-03-31 Lixia Cao,Tingting Tong,David Trafimow,Tonghui Wang,Xiangfei Chen
Although the literature on the a priori procedure, designed to help researchers determine the sample sizes they should use in their substantive research, is expanding rapidly, there are two important limitations. First, there is a need to expand to new popular distributions, log-normal and gamma distributions, and the present work provides those expansions. Second, there is a need to test the consequences
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Welcome message from the new editors-in-chief Methodology (IF 2.0) Pub Date : 2022-03-31 Marcelino Cuesta,Katrijn Van Deun
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Obtaining sound intraclass correlation and variance estimates in three-level models: The role of sampling-strategies Methodology (IF 2.0) Pub Date : 2022-03-31 Denise Kerkhoff,Fridtjof W. Nussbeck
Three-level clustered data commonly occur in social and behavioral research and are prominently analyzed using multilevel modeling. The influence of the clustering on estimation results is assessed with the intraclass correlation coefficients (ICCs), which indicate the fraction of variance in the outcome located at each higher level. However, ICCs are prone to bias due to high requirements regarding
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MSA: The forgotten index for identifying inappropriate items before computing exploratory item factor analysis Methodology (IF 2.0) Pub Date : 2021-12-17 Urbano Lorenzo-Seva,Pere J. Ferrando
Kaiser’s single-variable measure of sampling adequacy (MSA) is a very useful index for debugging inappropriate items before a factor analysis (FA) solution is fitted to an item-pool dataset for item selection purposes. For reasons discussed in the article, however, MSA is hardly used nowadays in this context. In our view, this is unfortunate. In the present proposal, we first discuss the foundation
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Nonlinear mixed-effects growth models: A tutorial using 'saemix' in R Methodology (IF 2.0) Pub Date : 2021-12-17 Peter Boedeker
Modeling growth across repeated measures of individuals and evaluating predictors of growth can reveal developmental patterns and factors that affect those patterns. When growth follows a sigmoidal shape, the Logistic, Gompertz, and Richards nonlinear growth curves are plausible. These functions have parameters that specifically control the starting point, total growth, overall rate of change, and
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Parametric and semi-parametric bootstrap-based confidence intervals for robust linear mixed models Methodology (IF 2.0) Pub Date : 2021-12-17 Fabio Mason,Eva Cantoni,Paolo Ghisletta
The linear mixed model (LMM) is a popular statistical model for the analysis of longitudinal data. However, the robust estimation of and inferential conclusions for the LMM in the presence of outliers (i.e., observations with very low probability of occurrence under Normality) is not part of mainstream longitudinal data analysis. In this work, we compared the coverage rates of confidence intervals
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Integrating informative hypotheses into the EffectLiteR framework Methodology (IF 2.0) Pub Date : 2021-12-17 Caroline Keck,Axel Mayer,Yves Rosseel
Using the EffectLiteR framework, researchers can test classical null hypotheses about effects of interest via Wald and F-tests, while taking into account the stochastic nature of group sizes. This paper aims at extending EffectLiteR to test informative hypotheses, assuming for example that the average effect of a new treatment is greater than the average effect of an old treatment, which in turn is
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Teaching mixed methods: Using the Titanic datasets to teach mixed methods data analysis Methodology (IF 2.0) Pub Date : 2021-09-30 Anaïd Lindemann,Jörg Stolz
The Titanic quantitative dataset has long been used to teach statistics. However, combining the quantitative dataset with a qualitative dataset of survivor testimonies shows that the Titanic case is an even better example to teach mixed methods. This article offers practical tools to teach mixed methods to undergraduate or postgraduate students in the social sciences, using the Titanic datasets. Based
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Does rapid guessing prevent the detection of the effect of a time limit in testing? Methodology (IF 2.0) Pub Date : 2021-09-30 Karl Schweizer,Dorothea Krampen,Brian F. French
Rapid guessing is a test taking strategy recommended for increasing the probability of achieving a high score if a time limit prevents an examinee from responding to all items of a scale. The strategy requires responding quickly and without cognitively processing item details. Although there may be no omitted responses after participants' rapid guessing, an open question remains: do the data show unidimensionality
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Quantile regression-based multiple imputation of missing values — An evaluation and application to corporal punishment data Methodology (IF 2.0) Pub Date : 2021-09-30 Kristian Kleinke,Markus Fritsch,Mark Stemmler,Jost Reinecke,Friedrich Lösel
Quantile regression (QR) is a valuable tool for data analysis and multiple imputation (MI) of missing values – especially when standard parametric modelling assumptions are violated. Yet, Monte Carlo simulations that systematically evaluate QR-based MI in a variety of different practically relevant settings are still scarce. In this paper, we evaluate the method regarding the imputation of ordinal
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Correcting the bias of the Root Mean Squared Error of Approximation under missing data Methodology (IF 2.0) Pub Date : 2021-09-30 Cailey E. Fitzgerald,Ryne Estabrook,Daniel P. Martin,Andreas M. Brandmaier,Timo von Oertzen
Missing data are ubiquitous in psychological research. They may come about as an unwanted result of coding or computer error, participants' non-response or absence, or missing values may be intentional, as in planned missing designs. We discuss the effects of missing data on χ²-based goodness-of-fit indices in Structural Equation Modeling (SEM), specifically on the Root Mean Squared Error of Approximation
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Modeling the Influences of Social Mobility Net of Origin and Destination Based on the Front-Door Criterion: A Simulation Study Methodology (IF 2.0) Pub Date : 2021-06-30 Anning Hu
The consequences of social mobility have been a persistent theme on the research agenda of social scientists, but the estimation of the net mobility effect controlling for both social origin and destination confronts with the identification problem. This research 1) highlights the mechanical identification approaches deployed by the conventional methods—the square additive model, the diamond model
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Person-centered data analysis with covariates and the R-package confreq Methodology (IF 2.0) Pub Date : 2021-06-30 Mark Stemmler,Jörg-Henrik Heine,Susanne Wallner
Configural Frequency Analysis (CFA) is a useful statistical method for the analysis of multiway contingency tables and an appropriate tool for person-oriented or person-centered methods. In complex contingency tables, patterns or configurations are analyzed by comparing observed cell frequencies with expected frequencies. Significant differences between observed and expected frequencies lead to the
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Latent profile analysis of human values: What is the optimal number of clusters? Methodology (IF 2.0) Pub Date : 2021-06-30 Mikkel N. Schmidt,Daniel Seddig,Eldad Davidov,Morten Mørup,Kristoffer Jon Albers,Jan Michael Bauer,Fumiko Kano Glückstad
Latent Profile Analysis (LPA) is a method to extract homogeneous clusters characterized by a common response profile. Previous works employing LPA to human value segmentation tend to select a small number of moderately homogeneous clusters based on model selection criteria such as Akaike information criterion, Bayesian information criterion and Entropy. The question is whether a small number of clusters
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Power analyses for moderator effects with (non)randomly varying slopes in cluster randomized trials Methodology (IF 2.0) Pub Date : 2021-06-30 Nianbo Dong,Jessaca Spybrook,Benjamin Kelcey,Metin Bulus
Researchers often apply moderation analyses to examine whether the effects of an intervention differ conditional on individual or cluster moderator variables such as gender, pretest, or school size. This study develops formulas for power analyses to detect moderator effects in two-level cluster randomized trials (CRTs) using hierarchical linear models. We derive the formulas for estimating statistical
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Gimme’s ability to recover group-level path coefficients and individual-level path coefficients Methodology (IF 2.0) Pub Date : 2021-03-31 Steffen Nestler,Sarah Humberg
The growing availability of intensive longitudinal data has increased psychological researchers' interest in ideographic-statistical methods that, for example, reveal the contemporaneous or lagged associations between different variables for a specific individual. However, when researchers assess several individuals, the results of such models are difficult to generalize across individuals. Researchers
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Multiple imputation to balance unbalanced designs for two-way analysis of variance Methodology (IF 2.0) Pub Date : 2021-03-31 Joost R. van Ginkel,Pieter M. Kroonenberg
A balanced ANOVA design provides an unambiguous interpretation of the F-tests, and has more power than an unbalanced design. In earlier literature, multiple imputation was proposed to create balance in unbalanced designs, as an alternative to Type-III sum of squares. In the current simulation study we studied four pooled statistics for multiple imputation, namely D₀, D₁, D₂, and D₃ in unbalanced data
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Analytic and bootstrap confidence intervals for the common-language effect size estimate Methodology (IF 2.0) Pub Date : 2021-03-31 Johnson Ching-Hong Li,Virginia Man Chung Tze
Evaluating how an effect-size estimate performs between two continuous variables based on the common-language effect size (CLES) has received increasing attention. While Blomqvist (1950; https://doi.org/10.1214/aoms/1177729754) developed a parametric estimator (q') for the CLES, there has been limited progress in further refining CLES. This study: a) extends Blomqvist’s work by providing a mathematical
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Multigroup CFA and alignment approaches for testing measurement invariance and factor score estimation: Illustration with the schoolwork-related anxiety survey across countries and gender Methodology (IF 2.0) Pub Date : 2021-03-31 Jason C. Immekus
Within large-scale international studies, the utility of survey scores to yield meaningful comparative data hinges on the degree to which their item parameters demonstrate measurement invariance (MI) across compared groups (e.g., culture). To-date, methodological challenges have restricted the ability to test the measurement invariance of item parameters of these instruments in the presence of many
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Much ado about nothing: Multiple imputation to balance unbalanced designs for two-way analysis of variance Methodology (IF 2.0) Pub Date : 2020-12-22 Joost R. van Ginkel,Pieter M. Kroonenberg
In earlier literature, multiple imputation was proposed to create balance in unbalanced designs, as an alternative to Type III sum of squares in two-way ANOVA. In the current simulation study we studied four pooled statistics for multiple imputation, namely D₀, D₁, D₂, and D₃ in unbalanced data, and compared these statistics with Type III sum of squares. Statistics D₀ and D₂ generally performed best
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Benchmarking and reconciliation with time-varying cross-coefficients Methodology (IF 2.0) Pub Date : 2020-12-22 José Luis Rojo-García,José Antonio Sanz-Gómez
In this paper, the authors propose a method to obtain explicit solutions for simultaneous benchmarking and reconciliation problems for a system of variables when the cross-restrictions use time-varying coefficients. The method is based on a hierarchical Bayesian model with a normal-gamma specification for the prior distributions. The proposed solution provides explicit (not sequential) feasible estimations
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The development of a new generic risk-of-bias measure for systematic reviews of surveys Methodology (IF 2.0) Pub Date : 2020-12-22 Gabriel Nudelman,Kathleen Otto
It is important to evaluate risk of bias of the primary studies included in systematic reviews and meta-analyses. Since tools pertinent to surveys are scarce, the goal of the current research was to develop a measure to address this need. In Study 1, an initial list of 10 relevant topics was compiled from previous measures. In Study 2, the list was refined into an eight-item risk-of-bias measure via
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Visual representations of meta-analyses of multiple outcomes: Extensions to forest plots, funnel plots, and caterpillar plots Methodology (IF 2.0) Pub Date : 2020-12-22 Belén Fernández-Castilla,Lies Declercq,Laleh Jamshidi,Susan Natasha Beretvas,Patrick Onghena,Wim Van den Noortgate
Meta-analytic datasets can be large, especially when in primary studies multiple effect sizes are reported. The visualization of meta-analytic data is therefore useful to summarize data and understand information reported in primary studies. The gold standard figures in meta-analysis are forest and funnel plots. However, none of these plots can yet account for the existence of multiple effect sizes
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A tutorial for meta-analysis of diagnostic tests for low-prevalence diseases: Bayesian models and software Methodology (IF 2.0) Pub Date : 2020-09-30 Johny J. Pambabay-Calero, Sergio A. Bauz-Olvera, Ana B. Nieto-Librero, Maria Purificación Galindo-Villardón, Ana B. Sánchez-García
Although measures such as sensitivity and specificity are used in the study of diagnostic test accuracy, these are not appropriate for integrating heterogeneous studies. Therefore, it is essential to assess in detail all related aspects prior to integrating a set of studies so that the correct model can then be selected. This work describes the scheme employed for making decisions regarding the use
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Adjusting group intercept and slope bias in predictive equations Methodology (IF 2.0) Pub Date : 2020-09-30 Bruce W. Austin, Brian F. French
Methods to assess measurement invariance in constructs have received much attention, as invariance is critical for accurate group comparisons. Less attention has been given to the identification and correction of the sources of non-invariance in predictive equations. This work developed correction factors for structural intercept and slope bias in common regression equations to address calls in the
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Sampling and fieldwork practices in Europe: Analysis of methodological documentation from 1,537 surveys in five cross-national projects, 1981-2017 Methodology (IF 2.0) Pub Date : 2020-09-30 Piotr Jabkowski, Marta Kołczyńska
This article addresses the comparability of sampling and fieldwork with an analysis of methodological data describing 1,537 national surveys from five major comparative cross-national survey projects in Europe carried out in the period from 1981 to 2017. We describe the variation in the quality of the survey documentation, and in the survey methodologies themselves, focusing on survey procedures with
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The effects of misspecifying the random part of multilevel models Methodology (IF 2.0) Pub Date : 2020-09-30 David M. LaHuis, Daniel R. Jenkins, Michael J. Hartman, Shotaro Hakoyama, Patrick C. Clark
This paper examined the amount bias in standard errors for fixed effects when the random part of a multilevel model is misspecified. Study 1 examined the effects of misspecification for a model with one Level 1 predictor. Results indicated that misspecifying random slope variance as fixed had a moderate effect size on the standard errors of the fixed effects and had a greater effect than misspecifying
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A meta-analysis of construct reliability indices and measurement model fit metrics Methodology (IF 2.0) Pub Date : 2020-09-30 Robert A. Peterson, Yeolib Kim, Boreum Choi
The present research examined the distributional properties of construct reliability indices and model fit metrics, explored relationships between and among the indices and metrics, and investigated variables influencing the relative magnitudes of the indices and metrics in structural equation measurement models. A broad-based meta-analysis of reported construct reliability indices and selected model
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Multi-choice wavelet thresholding based binary classification method Methodology (IF 2.0) Pub Date : 2020-06-18 Seung Hyun Baek, Alberto Garcia-Diaz, Yuanshun Dai
Data mining is one of the most effective statistical methodologies to investigate a variety of problems in areas including pattern recognition, machine learning, bioinformatics, chemometrics, and statistics. In particular, statistically-sophisticated procedures that emphasize on reliability of results and computational efficiency are required for the analysis of high-dimensional data. Optimization
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Specifying the random effect structure in linear mixed effect models for analyzing psycholinguistic data Methodology (IF 2.0) Pub Date : 2020-06-18 Jungkyu Park, Ramsey Cardwell, Hsiu-Ting Yu
Linear Mixed Effect Models (LMEM) have become a popular method for analyzing nested experimental data, which are often encountered in psycholinguistics and other fields. This approach allows experimental results to be generalized to the greater population of both subjects and experimental stimuli. In an influential paper Bar and his colleagues (2013; https://doi.org/10.1016/j.jml.2012.11.001) recommend
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Modeling heterogeneity of the level-1 error covariance matrix in multilevel models for single-case data Methodology (IF 2.0) Pub Date : 2020-06-18 Eunkyeng Baek, John J. M. Ferron
Previous research applying multilevel models to single-case data has made a critical assumption that the level-1 error covariance matrix is constant across all participants. However, the level-1 error covariance matrix may differ across participants and ignoring these differences can have an impact on estimation and inferences. Despite the importance of this issue, the effects of modeling between-case
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Performance of missing data approaches under nonignorable missing data conditions Methodology (IF 2.0) Pub Date : 2020-06-18 Steffi Pohl, Benjamin Becker
Approaches for dealing with item omission include incorrect scoring, ignoring missing values, and approaches for nonignorable missing values and have only been evaluated for certain forms of nonignorability. In this paper we investigate the performance of these approaches for various conditions of nonignorability, that is, when the missing response depends on i) the item response, ii) a latent missing
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The inaccuracy of sample-based confidence intervals to estimate a priori ones Methodology (IF 2.0) Pub Date : 2020-06-18 David Trafimow, Joshua Uhalt
Confidence intervals (CIs) constitute the most popular alternative to widely criticized null hypothesis significance tests. CIs provide more information than significance tests and lend themselves well to visual displays. Although CIs are no better than significance tests when used solely as significance tests, researchers need not limit themselves to this use of CIs. Rather, CIs can be used to estimate
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Group sequential designs applied in psychological research Methodology (IF 2.0) Pub Date : 2020-04-06 Klemens Weigl, Ivo Ponocny
Psychological research is confronted with ever-increasing demands to save resources such as time and money while assuring high ethical standards. In medical and pharmaceutical research, group sequential designs have fundamentally changed traditional statistical testing approaches featuring only one analysis at the end of a single-stage study. They enable early stopping at an interim stage, after a
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The use of items and item parcels in nonlinear structural equation models Methodology (IF 2.0) Pub Date : 2020-04-06 Karina Rdz-Navarro, Rodrigo A. Asún
Nonlinear structural equation models within the frequentist framework were developed to work with continuous items. Applied researchers who usually work with Likert-type items choose between two strategies to estimate such models: treat items as continuous variables or create item parcels. Two Monte Carlo studies were conducted to evaluate the effects of each strategy on estimates and Type I errors
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A modified tucker’s congruence coefficient for factor matching Methodology (IF 2.0) Pub Date : 2020-04-06 Anikó Lovik, Vahid Nassiri, Geert Verbeke, Geert Molenberghs
Since factor analysis is one of the most often used techniques in psychometrics, comparing or combining solutions from different factor analyses is often needed. Several measures to compare factors exist, one of the best known is Tucker’s congruence coefficient, which is enjoying newly found popularity thanks to the recent work of Lorenzo-Seva and ten Berge (2006), who established cut-off values for
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What can we learn from open questions in surveys? A case study on non-voting reported in the 2013 German longitudinal election study Methodology (IF 2.0) Pub Date : 2020-04-06 Henning Silber, Cornelia Zuell, Steffen-M. Kuehnel
Open survey questions are often used to evaluate closed questions. However, they can fulfil this function only if there is a strong link between answers to open questions and answers to related closed questions. Using reasons for non-voting reported in the German Longitudinal Election Study 2013, we investigated this link by examining whether the reported reasons for non-voting may be substantive reasons
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Comparing alignment and multiple group CFA for analysing political trust in europe during the crisis Methodology (IF 2.0) Pub Date : 2020-04-06 Lluís Coromina, Edurne Bartolomé Peral
Institutional trust is in decline in many western democracies. Since the 2008 global economic and financial crisis, this increasing distrust has been closely related to trust in political institutions. Trust in institutions is one of the pillars of democracy, and its decline is one of the most evident and shared symptoms of the recession, especially in those contexts where it has been particularly
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Strategies for Increasing the Accuracy of Interviewer Observations of Respondent Features: Evidence from the U.S. National Survey of Family Growth. Methodology (IF 2.0) Pub Date : 2018-05-08 Brady T West,Frauke Kreuter
Because survey response rates are consistently declining worldwide, survey researchers strive to obtain as much auxiliary information on sampled units as possible. Surveys using in-person interviewing often request that interviewers collect observations on key features of all sampled units, given that interviewers are the eyes and ears of the survey organization. Unfortunately, these observations are
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Comparing the Performance of Improved Classify-Analyze Approaches For Distal Outcomes in Latent Profile Analysis. Methodology (IF 2.0) Pub Date : 2017-06-21 John J Dziak,Bethany C Bray,Jieting Zhang,Minqiang Zhang,Stephanie T Lanza
Several approaches are available for estimating the relationship of latent class membership to distal outcomes in latent profile analysis (LPA). A three-step approach is commonly used, but has problems with estimation bias and confidence interval coverage. Proposed improvements include the correction method of Bolck, Croon, and Hagenaars (BCH; 2004), Vermunt's (2010) maximum likelihood (ML) approach
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8th Congress of the European Association of Methodology (EAM) Methodology (IF 2.0) Pub Date : 2019-10-01 Rolf Steyer
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The EV Scaling Method for Variances of Latent Variables Methodology (IF 2.0) Pub Date : 2019-10-01 Karl Schweizer, Stefan Troche
Abstract. The paper describes EV scaling for variances of latent variables included in confirmatory factor models. EV-scaled variances can be achieved in two ways: the estimation of variance parame...
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Continuous-Time Latent Markov Factor Analysis for Exploring Measurement Model Changes Across Time Methodology (IF 2.0) Pub Date : 2019-10-01 Leonie V. D. E. Vogelsmeier, Jeroen K. Vermunt, Florian Böing-Messing, Kim De Roover
Abstract. Drawing valid inferences about daily or long-term dynamics of psychological constructs (e.g., depression) requires the measurement model (indicating which constructs are measured by which...
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Causal Effects Based on Latent Variable Models Methodology (IF 2.0) Pub Date : 2019-10-01 Axel Mayer
Abstract. Building on the stochastic theory of causal effects and latent state-trait theory, this article shows how a comprehensive analysis of the effects of interventions can be conducted based o...
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The Social Relations Model for Count Data Methodology (IF 2.0) Pub Date : 2019-10-01 Justine Loncke, William L. Cook, Jenae M. Neiderhiser, Tom Loeys
Abstract. The social relations model (SRM) is typically used to identify sources of variance in interpersonal dispositions in families. Traditionally, it uses dyadic measurements that are obtained ...
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A Tutorial in Bayesian Mediation Analysis With Latent Variables Methodology (IF 2.0) Pub Date : 2019-10-01 Milica Miočević
Abstract. Maximum Likelihood (ML) estimation is a common estimation method in Structural Equation Modeling (SEM), and parameters in such analyses are interpreted using frequentist terms and definit...
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Fitting Bayesian Models for Single-Case Experimental Designs Methodology (IF 2.0) Pub Date : 2019-10-01 Prathiba Natesan
Abstract. Single-case experimental designs (SCEDs) are interrupted time-series designs that have recently gained recognition as being able to provide a strong basis for establishing intervention ef...
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Using Response Times as Collateral Information About Latent Traits in Psychological Tests Methodology (IF 2.0) Pub Date : 2019-10-01 Jochen Ranger, Anett Wolgast
Abstract. In psychological tests, the time needed to respond to the items provides collateral information about the latent traits of the test takers. This, however, requires a measurement model tha...
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The Statistics of Replication Methodology (IF 2.0) Pub Date : 2019-10-01 Larry V. Hedges
Abstract. The concept of replication is fundamental to the logic and rhetoric of science, including the argument that science is self-correcting. Yet there is very little literature on the methodol...
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A Comparison of Simple Structure Rotation Criteria in Temporal Exploratory Factor Analysis for Event-Related Potential Data Methodology (IF 2.0) Pub Date : 2019-10-01 Florian Scharf, Steffen Nestler
Abstract. It is challenging to apply exploratory factor analysis (EFA) to event-related potential (ERP) data because such data are characterized by substantial temporal overlap (i.e., large cross-l...
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Optimal Sample Sizes for Testing the Equivalence of Two Means Methodology (IF 2.0) Pub Date : 2019-08-01 Jiin-Huarng Guo, Hubert J. Chen, Wei-Ming Luh
Abstract. Equivalence tests (also known as similarity or parity tests) have become more and more popular in addition to equality tests. However, in testing the equivalence of two population means, ...
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Assessing the Quality and Effectiveness of the Factor Score Estimates in Psychometric Factor-Analytic Applications Methodology (IF 2.0) Pub Date : 2019-08-01 Pere J. Ferrando, David Navarro-González, Urbano Lorenzo-Seva
Abstract. This article proposes an approach, intended for factor-analytic (FA) psychometric applications, which aims to assess the extent to which the FA-derived individual score estimates are accu...
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Robustness of Statistical Power in Group-Randomized Studies of Mediation Under an Optimal Sampling Framework Methodology (IF 2.0) Pub Date : 2019-08-01 Kyle Cox, Benjamin Kelcey
Abstract. When planning group-randomized studies probing mediation, effective and efficient sample allocation is governed by several parameters including treatment-mediator and mediator-outcome pat...
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Clinically Meaningful Change Methodology (IF 2.0) Pub Date : 2019-08-01 Rodrigo Ferrer, Antonio Pardo
Abstract. In a recent paper, Ferrer and Pardo (2014) tested several distribution-based methods designed to assess when test scores obtained before and after an intervention reflect a statistically ...
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Applying AdaBoost to Improve Diagnostic Accuracy Methodology (IF 2.0) Pub Date : 2019-04-01 Zhehan Jiang, Kevin Walker, Dexin Shi
Abstract. Cognitive diagnostic modeling has been adopted to support various diagnostic measuring processes. Specifically, this approach allows practitioners and/or researchers to investigate an ind...
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Using Shrinkage in Multilevel Models to Understand Intersectionality Methodology (IF 2.0) Pub Date : 2019-04-01 Andrew Bell, Daniel Holman, Kelvyn Jones
Abstract. Multilevel models have recently been used to empirically investigate the idea that social characteristics are intersectional such as age, sex, ethnicity, and socioeconomic position intera...
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Improving Bi-Factor Exploratory Modeling Methodology (IF 2.0) Pub Date : 2019-04-01 Eduardo Garcia-Garzon, Francisco José Abad, Luis Eduardo Garrido
Abstract. Bi-factor exploratory modeling has recently emerged as a promising approach to multidimensional psychological measurement. However, state-of-the-art methods relying on target rotation req...