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Multivariate Behavioral Research

Patrick O'Keefe, Joseph Lee Rodgers
This paper introduces an extension of cluster mean centering (also called group mean centering) for multilevel models, which we call "double decomposition (DD)." This centering method separates between-level variance, as in cluster mean centering, but also decomposes within-level variance of the same variable. This process retains the benefits of cluster mean centering but allows for context variables derived from lower level variables, other than the cluster mean, to be incorporated into the model. A brief simulation study is presented, demonstrating the potential advantage (or even necessity) for DD in certain circumstances...
September 11, 2017: Multivariate Behavioral Research
Ke-Hai Yuan, Miao Yang, Ge Jiang
Survey data often contain many variables. Structural equation modeling (SEM) is commonly used in analyzing such data. With typical nonnormally distributed data in practice, a rescaled statistic Trml proposed by Satorra and Bentler was recommended in the literature of SEM. However, Trml has been shown to be problematic when the sample size N is small and/or the number of variables p is large. There does not exist a reliable test statistic for SEM with small N or large p, especially with nonnormally distributed data...
September 11, 2017: Multivariate Behavioral Research
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August 30, 2017: Multivariate Behavioral Research
Jodi M Casabianca, Brian W Junker, Ricardo Nieto, Mark A Bond
Research studies in psychology and education often seek to detect changes or growth in an outcome over a duration of time. This research provides a solution to those interested in estimating latent traits from psychological measures that rely on human raters. Rater effects potentially degrade the quality of scores in constructed response and performance assessments. We develop an extension of the hierarchical rater model (HRM), which yields estimates of latent traits that have been corrected for individual rater bias and variability, for ratings that come from longitudinal designs...
August 28, 2017: Multivariate Behavioral Research
Dirk Lubbe, Christof Schuster
Extreme response style or, more generally, individual differences in response spacing have been shown to be an influential bias when analyzing questionnaire data. Recently a promising model adjusting for this bias - the differential discrimination model - has been proposed. An advantage to other related approaches is that the model can be fitted using standard structural equation modeling software. However, the model is designed for analyzing continuous item responses, whereas graded response formats are certainly more prominent in behavioral sciences...
August 28, 2017: Multivariate Behavioral Research
Peter H Westfall, Andrea L Arias, Larry V Fulton
Introducing principal components (PCs) to students is difficult. First, the matrix algebra and mathematical maximization lemmas are daunting, especially for students in the social and behavioral sciences. Second, the standard motivation involving variance maximization subject to unit length constraint does not directly connect to the "variance explained" interpretation. Third, the unit length and uncorrelatedness constraints of the standard motivation do not allow re-scaling or oblique rotations, which are common in practice...
July 17, 2017: Multivariate Behavioral Research
Daniel McNeish
Studies on small sample properties of multilevel models have become increasingly prominent in the methodological literature in response to the frequency with which small sample data appear in empirical studies. Simulation results generally recommend that empirical researchers employ restricted maximum likelihood estimation (REML) with a Kenward-Roger correction with small samples in frequentist contexts to minimize small sample bias in estimation and to prevent inflation of Type-I error rates. However, simulation studies focus on recommendations for best practice, and there is little to no explanation of why traditional maximum likelihood (ML) breaks down with smaller samples, what differentiates REML from ML, or how the Kenward-Roger correction remedies lingering small sample issues...
July 17, 2017: Multivariate Behavioral Research
Sierra A Bainter
Psychometric models for item-level data are broadly useful in psychology. A recurring issue for estimating item factor analysis (IFA) models is low-item endorsement (item sparseness), due to limited sample sizes or extreme items such as rare symptoms or behaviors. In this paper, I demonstrate that under conditions characterized by sparseness, currently available estimation methods, including maximum likelihood (ML), are likely to fail to converge or lead to extreme estimates and low empirical power. Bayesian estimation incorporating prior information is a promising alternative to ML estimation for IFA models with item sparseness...
July 17, 2017: Multivariate Behavioral Research
Youn Seon Lim, Fritz Drasgow
A nonparametric technique based on the Hamming distance is proposed in this research by recognizing that once the attribute vector is known, or correctly estimated with high probability, one can determine the item-by-attribute vectors for new items undergoing calibration. We consider the setting where Q is known for a large item bank, and the q-vectors of additional items are estimated. The method is studied in simulation under a wide variety of conditions, and is illustrated with the Tatsuoka fraction subtraction data...
July 17, 2017: Multivariate Behavioral Research
John J Dziak, Kimberly L Henry
Researchers often build regression models to relate a response to a set of predictor variables. In some cases, there are predictors that apply to some participants, or to some measurement occasions, but not others. For example, a romantic partner's substance use may be a key predictor of one's own substance use. However, not all participants have a partner, and in a longitudinal study, participants may have a partner during only some occasions. This could be viewed as missing data, but of a very distinctive type: the values are not just unknown but also undefined...
June 16, 2017: Multivariate Behavioral Research
R Philip Chalmers, Jolynn Pek, Yang Liu
Confidence intervals (CIs) are fundamental inferential devices which quantify the sampling variability of parameter estimates. In item response theory, CIs have been primarily obtained from large-sample Wald-type approaches based on standard error estimates, derived from the observed or expected information matrix, after parameters have been estimated via maximum likelihood. An alternative approach to constructing CIs is to quantify sampling variability directly from the likelihood function with a technique known as profile-likelihood confidence intervals (PL CIs)...
June 8, 2017: Multivariate Behavioral Research
Janne K Adolf, Manuel C Voelkle, Annette Brose, Florian Schmiedek
Much of recent affect research relies on intensive longitudinal studies to assess daily emotional experiences. The resulting data are analyzed with dynamic models to capture regulatory processes involved in emotional functioning. Daily contexts, however, are commonly ignored. This may not only result in biased parameter estimates and wrong conclusions, but also ignores the opportunity to investigate contextual effects on emotional dynamics. With fixed moderated time series analysis, we present an approach that resolves this problem by estimating context-dependent change in dynamic parameters in single-subject time series models...
May 22, 2017: Multivariate Behavioral Research
Yu Liu, Stephen G West, Roy Levy, Leona S Aiken
In multiple regression researchers often follow up significant tests of the interaction between continuous predictors X and Z with tests of the simple slope of Y on X at different sample-estimated values of the moderator Z (e.g., ±1 SD from the mean of Z). We show analytically that when X and Z are randomly sampled from the population, the variance expression of the simple slope at sample-estimated values of Z differs from the traditional variance expression obtained when the values of X and Z are fixed. A simulation study using randomly sampled predictors compared four approaches: (a) the Aiken and West ( 1991 ) test of simple slopes at fixed population values of Z, (b) the Aiken and West test at sample-estimated values of Z, (c) a 95% percentile bootstrap confidence interval approach, and (d) a fully Bayesian approach with diffuse priors...
May 2, 2017: Multivariate Behavioral Research
Danielle O Dean, Daniel J Bauer, Mitchell J Prinstein
A social network perspective can bring important insight into the processes that shape human behavior. Longitudinal social network data, measuring relations between individuals over time, has become increasingly common-as have the methods available to analyze such data. A friendship duration model utilizing discrete-time multilevel survival analysis with a multiple membership random effect structure is developed and applied here to study the processes leading to undirected friendship dissolution within a larger social network...
May 2017: Multivariate Behavioral Research
Yu Liu, Craig K Enders
In Ordinary Least Square regression, researchers often are interested in knowing whether a set of parameters is different from zero. With complete data, this could be achieved using the gain in prediction test, hierarchical multiple regression, or an omnibus F test. However, in substantive research scenarios, missing data often exist. In the context of multiple imputation, one of the current state-of-art missing data strategies, there are several different analogous multi-parameter tests of the joint significance of a set of parameters, and these multi-parameter test statistics can be referenced to various distributions to make statistical inferences...
May 2017: Multivariate Behavioral Research
Kuan-Yu Jin, Wen-Chung Wang
Multifaceted data are very common in the human sciences. For example, test takers' responses to essay items are marked by raters. If multifaceted data are analyzed with standard facets models, it is assumed there is no interaction between facets. In reality, an interaction between facets can occur, referred to as differential facet functioning. A special case of differential facet functioning is the interaction between ratees and raters, referred to as differential rater functioning (DRF). In existing DRF studies, the group membership of ratees is known, such as gender or ethnicity...
May 2017: Multivariate Behavioral Research
Niels G Waller, Leah Feuerstahler
In this study, we explored item and person parameter recovery of the four-parameter model (4PM) in over 24,000 real, realistic, and idealized data sets. In the first analyses, we fit the 4PM and three alternative models to data from three Minnesota Multiphasic Personality Inventory-Adolescent form factor scales using Bayesian modal estimation (BME). Our results indicated that the 4PM fits these scales better than simpler item Response Theory (IRT) models. Next, using the parameter estimates from these real data analyses, we estimated 4PM item parameters in 6,000 realistic data sets to establish minimum sample size requirements for accurate item and person parameter recovery...
May 2017: Multivariate Behavioral Research
Natalie A Koziol, James A Bovaird, Sonia Suarez
Sampling designs of large-scale survey studies are typically complex, involving multiple design features such as clustering and unequal probabilities of selection. Single-level (i.e., population-averaged) methods that use adjusted variance estimators and multilevel (i.e., cluster-specific) methods provide two alternatives for modeling clustered data. Although the literature comparing these methods is vast, comparisons have been limited to the context in which all sampling units are selected with equal probabilities (thus circumventing the need for sampling weights)...
May 2017: Multivariate Behavioral Research
Kyle M Lang, Wei Wu
Many variables that are analyzed by social scientists are nominal in nature. When missing data occur on these variables, optimal recovery of the analysis model's parameters is a challenging endeavor. One of the most popular methods to deal with missing nominal data is multiple imputation (MI). This study evaluated the capabilities of five MI methods that can be used to treat incomplete nominal variables: multiple imputation with chained equations (MICE) using polytomous regression as the elementary imputation method; MICE based on classification and regression trees (CART); MICE based on nested logistic regressions; the ranking procedure described by Allison ( 2002 ); and a joint modeling approach based on the general location model...
May 2017: Multivariate Behavioral Research
Samantha F Anderson, Scott E Maxwell
Psychology is undergoing a replication crisis. The discussion surrounding this crisis has centered on mistrust of previous findings. Researchers planning replication studies often use the original study sample effect size as the basis for sample size planning. However, this strategy ignores uncertainty and publication bias in estimated effect sizes, resulting in overly optimistic calculations. A psychologist who intends to obtain power of .80 in the replication study, and performs calculations accordingly, may have an actual power lower than ...
May 2017: Multivariate Behavioral Research
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