1,721,228 research outputs found
Implications of alternative parameterizations in structural equation models for longitudinal categorical variables
Get PDFWhen 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
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Get PDFWe 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
Assessment and Robust Analysis of Survey Errors
Get PDFRandom measurement error and errors due to complex sampling designs may have deleterious effects on the quality of parameter estimates. This dissertation is comprised of three research papers that provide 1) an assessment of random measurement error through estimation of reliability using longitudinal, latent variable models, 2) an evaluation of the various probability weighting methods as corrections to unequal selection probabilities in multilevel models, and 3) an evaluation of several probability weighting and modeling approaches to unequal inclusion of observations in growth curve models. A popular structural equation model used to estimate reliability for a single measure observed over time is the quasi-simplex model. The quasi-simplex model (QSM) requires assumptions about the constancy of variance components over time, which may not be valid for a given sample and population. These assumptions are tested using models that extend the QSM by using multiple indicator factors. The extended models include item specific error variance and additional factor variance estimates. Reliability estimates and their standard errors for the models with and without the QSM assumptions are compared in light of model fit and test results for several scales using survey data. Reliability estimates for a general model without the QSM assumptions are generally similar to the models with the assumptions indicating that the particular QSM assumption may not be that critical to the reliability estimates obtained from these models. However, variance components due to additional factor and item specific error have the potential of affecting reliability estimates markedly when they are estimated by the model. Probability weights have traditionally been designed for single level analysis and not for use in multilevel models. A method for applying probability weights in multilevel models has been developed and has good performance with large sample sizes at each level of the model (Pfeffermann, Skinner, Holmes, Goldstein, and Rasbach, 1998). But, the multilevel weighting method in Pfeffermann, et al. can result in relatively poor estimation due to large amount of variation in the multilevel weights. This chapter includes a simulation analysis to evaluate several alternative methods for analyzing two-level models in the presence of unequal selection probabilities. The primary method of interest is to specify the level two part of the model such that it is robust to unequal selection bias in combination with weighting for unequal selection at level one. This "mixed" method does result in less bias, lower variance, and lower mean squared error for some models. A limitation is that the mixed method requires that the model is correctly specified at level two and the appropriate level one weight is used. This "mixed" method is a new approach in that it combines the Pfeffermann et al. (1998) weighting methodology at level one with the use of sample design variables at level two, rather than use the full Pfeffermann et al. approach of weighting at both levels. Panel studies often suffer from attrition and intermittent nonresponse. Panel data is also commonly selected using complex sampling techniques that include unequal selection of observations. Unequal inclusion of individuals and of repeated measures will result in biased estimates when the missing mechanism is nonignorable, that is, when missing values are related to outcomes. Probability weighting may be used to correct estimates for nonignorable unequal inclusion due to selection and intermittent nonresponse. However the growth curve models frequently used in analysis of change have not traditionally been estimated using sampling weights. These models are usually estimated using a mixed model where the repeated measures are modeled as a function of both fixed and random parameters. Whereas sampling or probability weights have traditionally been applied to marginal models, which do not include random effects parameters. In this chapter, several weighting approaches are applied to the mixed and marginal modeling frameworks using simulated and empirical data in linear growth models with continuos outcomes. Probability weighting performs the best in a marginal model when missing data are nonignorable. However, in most real situations including the empirical example provided in this chapter, probability weights may need to be combined with estimation that also utilizes variance weighting such as the GLS estimator with a correctly specified repeated measures correlation matrix as the variance weight matrix. This estimation methods can improve efficiency and decrease bias in estimates when data are missing at random (MAR)
Testing the Blowback Thesis: American Military Presence and Terrorism against Americans
Get PDFSocial scientific analyses of anti-American terrorism primarily seek explanations in the political, economic, and social conditions of states where Americans are attacked. A prominent counter-narrative places the focus on U.S. foreign policies positing that anti-American terrorism is blowback for them. The few studies that analyze anti-American terrorism as a potential consequence of U.S. actions abroad do not examine if there are long-term or cumulative effects of U.S. policies and actions. In order to effectively determine if terror events are truly blowback we must integrate an examination of long-term effects. To inject cumulative effects into the discussion, this paper evaluates six methodological approaches to that end and applies them to two replicated studies that are most consistent to the concept of blowback. The analysis demonstrates that military dependency is integral to explanations of anti-American terrorism and that blowback is greater than previously identified when long-term effects are considered.Master of Art
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