1,721,165 research outputs found
Implications of alternative parameterizations in structural equation models for longitudinal categorical variables
Full text linkWhen analyzing scaling conditions in latent variable Structural Equation Models (SEMs) with continuous observed variables, analysts scaling a latent variable typically set the factor loading of one indicator to one and either set its intercept to zero or the mean of its latent variable to zero.
When binary and ordinal observed variables are part of SEMs, the identification and scaling choices are more varied. Longitudinal data further complicate this. In SEM software, such as lavaan and Mplus, fixing the underlying variables’ variances or the error variances to one are two primary scaling conventions. As demonstrated in this paper, choosing between these constraints can significantly impact
longitudinal analysis, affecting model fit, degrees of freedom, and assumptions about the dynamic process and error structure. We explore alternative parameterizations and conditions of model equivalence with categorical repeated measures. Using data from the National Longitudinal Survey of Youth 1997, we empirically explore how different parameterizations lead to varying conclusions in longitudinal categorical analysis. More specifically, we provide insights into the specifications of the autoregressive latent trajectory model and its special cases - the linear growth curve and first-order autoregressive models - for categorical repeated measures. These findings have broader implications for a wide range of longitudinal models
The Latent Variable-Autoregressive Latent Trajectory Model: A General Framework for Longitudinal Data Analysis
No full textIn recent years, longitudinal data have become increasingly relevant in many applications, heightening interest in selecting the best longitudinal model to analyze them. Too often, traditional practice rather than substantive theory guides the specific model selected. This opens the possibility that alternative models might better correspond to the data. In this paper, we present a general longitudinal model that we call the Latent Variable-Autoregressive Latent Trajectory (LV-ALT) model that includes most other longitudinal models with continuous outcomes as special cases. It is capable of specializing to most models dictated by theory or prior research while having the capacity to compare them to alternative ones. If there is little guidance on the best model, the LV-ALT provides a way to determine the appropriate empirical match to the data. We present the model, discuss its identification and estimation, and illustrate how the LV-ALT reveals new things about a widely used empirical example
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
GOOD THINGS COME TO THOSE WHO WEIGHT? TESTING WEIGHTING NECESSITY METRICS FOR COMPLEX SURVEY DATA
Full text linkI studied the performance of four statistical tests designed to assess the necessity of weighting in the analysis of survey data. First, I investigated the finite sample properties of diagnostic tests using Monte Carlo simulations, including a case in which weights are ignorable and a case where weights are designed to be necessary. I tested the performance of four necessity metrics across both scenarios. Second, I took a sample of studies using data and weights from Add Health. I replicated models proposed in each study and applied weighting necessity diagnostic tests. Results from the simulation study found that the DuMouchel-Duncan test exhibited the strongest performance most consistently. Demonstrating the use of weight necessity tests on replicated models revealed that Difference in Coefficient Tests returned significant test statistics most often. These results offer insight into the selection of appropriate weight necessity tests, contributing to more accurate analysis in complex survey settings.Master of Art
On the Estimation of Controllability Metrics in Ordinal Vector Autoregressive Models
Full text linkThe quantification of symptom importance in psychological disorders is a central problem in clinicalscience. Many diverse methods have been proposed to tackle this problem, and one such technique isthrough the application of control theory to psychological time series. In this approach, the evolution ofmultiple time-varying indices (such as symptoms and behaviors) across time is treated as a dynamicalsystem and symptom importance is quantified through the controllability gramian of the underlyingsystem. Existing work, however, assumes that individual indices are measured on continuous scales.This is in contrast to the inherent ordinal nature of most psychological measures. In this regard, it islargely unknown how effectively we can recover the true underlying gramian when ordinal measures areof interest, especially in small sample regimes. In this work, we study the relevance and recoverability of the controllability gramian obtained byestimating (ordinal) vector autoregressive models in an SEM framework. This is done in 3 parts. Inthe first part, we demonstrate that controllability metrics obtained from the gramian provide distinctinformation vis-a-vis traditional symptom metrics obtained from graph theory. Secondly, we studied ifordinal modeling techniques using polychoric correlations improve upon a naive treatment of ordinaldata as continuous variables when estimating the controllability gramian. Lastly, we examined how wellwe can recover the difference in gramian values in pre-post intervention designs. Results indicate that,unless the length of the observed time-series is unreasonably large, the use of polychoric correlationsprovided little benefit relative to the naive approach of assuming continuous responses. Furthermore, wefound that accurate estimation of differences in the gramian in a pre-post setting can be challenging, withthe results varying depending on the metric used.Master of Art
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