Showing papers in "Sociological Methods & Research in 1992"

Alternative Ways of Assessing Model Fit

Abstract: This article is concerned with measures of fit of a model. Two types of error involved in fitting a model are considered. The first is error of approximation which involves the fit of the model, wi...

Bootstrapping Goodness-of-Fit Measures in Structural Equation Models

Abstract: Assessing overall fit is a topic of keen interest to structural equation modelers, yet measuring goodness of fit has been hampered by several factors. First, the assumptions that underlie the chi-square tests of model fit often are violated. Second, many fit measures (e.g., Bentler and Bonett's [1980] normed fit index) have unknown statistical distributions so that hypothesis testing, confidence intervals, or comparisons of significant differences in these fit indices are not possible. Finally, modelers have little knowledge about the distribution and behavior of the fit measures for misspecified models or for nonnested models. Given this situation, bootstrapping techniques would appear to be an ideal means to tackle these problems. Indeed, Bentler's (1989) EQS 3.0 and Joreskog and Sorbom's (forthcoming) LISREL 8 have bootstrap resampling options to bootstrap fit indices. In this article the authors (a) demonstrate that the usual bootstrapping methods will fail when applied to the original data, (b) expla...

Monte Carlo Evaluations of Goodness of Fit Indices for Structural Equation Models

Abstract: This article reviews proposed goodness-of-fit indices for structural equation models and the Monte Carlo studies that have empirically assessed their distributional properties. The cumulative contributions of the studies are summarized, and the variables under which the indices are studied are noted. A primary finding is that many of the indices used until the late 1980s, including Joreskog and Sorbom's (1981) GFI and Bentler and Bonett's (1980) NFI, indicated better fit when sample size increased. More recently developed indices based on the chi-square noncentrality parameter are discussed and the relevant Monte Carlo studies reviewed. Although a more complete understanding of their properties and suitability requires further research, the recommended fit indices are the McDonald (1989) noncentrality index, the Bentler (1990)-McDonald and Marsh (1990) RNI (or the bounded counterpart CFI), and Bollen's (1989) DELTA2.

From Causes to Events Notes on Narrative Positivism

Abstract: This article reviews new and rigorous methods for analyzing narrative data and over-time data more generally. It locates these new methods within a general turn away from the “variables paradigm.”

Assumptions and Comparative Strengths of the Two-Step Approach Comment on Fornell and Yi

Abstract: Fornell and Yi (1992 [this issue]) have discussed four assumptions that they contend underlie two-step approaches to structural equation modeling. Each of these assumptions is demonstrated to not be an assumption of the two-step approach recommended by Anderson and Gerbing (1988). In doing so, an attempt is made to provide some clarification and guidance to researchers interested in employing structural equation modeling to test and develop theory. Given the comparative strengths of the two-step approach over a one-step approach in practice, it is concluded that the preferred approach to the modeling task is the two-step approach of Anderson and Gerbing (1988).

Historical Sociology and Time

Abstract: Historical sociologists have criticized their discipline for a tendency to ignore the temporal dimensions of social life, either by studying the correlates of outcomes rather than the character of temporally connected events or by treating events as surface manifestations of large-scale and long-term processes of change. These critiques have led to a reassessment of the value of narratives and to new methods for mapping historical sequences of events. Yet there has been relatively little discussion of the concepts needed to create a more event-centered historical sociology. This article explores the way in which four different concepts of time-duration, pace, trajectory, and cycle-have been used in recent historical social science. These concepts allow one to analyze the temporal characteristics of connected events that constitute long-term historical processes as well as the way in which actors understand and experience the temporal flow of events. They are most useful, the author argues when employed in...

Correcting for Acquiescent Response Bias in the Absence of a Balanced Scale: An Application to Class Consciousness

Abstract: This article describes a method for purging data of the effects of acquiescent response bias—a tendency to respond in the affirmative to survey items irrespective of substantive content—in the abse...

Assumptions of the Two-Step Approach to Latent Variable Modeling

Abstract: Although methods for latent variable modeling that allow a joint analysis of measurement and theory have become popular, they are not without difficulties. As these difficulties have become more apparent, several researchers have recently called for a “two-step approach” to latent variable modeling in which measurement is evaluated separately from theory. This implies that programs for covariance structure analysis are not needed because factor analysis and regressions would suffice for analysis. Before a return to earlier practice using seemingly simpler analysis tools can be recommended, it seems prudent to consider the assumptions underlying a two-step approach. At least four implicit assumptions can be identified: (a) theory and measurement are independent, (b) results of factor analysis specifications can be generalized to other specifications, (c) the estimators have desirable statistical properties, and (d) the statistical test in one step is independent of the test in the other. The authors show t...

Have Historical Sociologists Forsaken Theory? Thoughts on the History/Theory Relationship

Abstract: With the re-emergence of historical sociology as a dominant focus of inquiry has come a renewed interest in more general methodological, theoretical, and epistemological issues that have long occup...

Analyzing Locational Attainments: Constructing Individual-Level Regression Models Using Aggregate Data

Abstract: The authors address a major difficulty associated with research on spatial processes. Although theory frequently demands analytic models that combine variables from individual and locational (or aggregate) levels, appropriate data sets are rarely available. The authors demonstrate that, in many circumstances, it is nevertheless possible to construct individual-level multivariate models by using aggregate census files and microdata samples, and they describe a method for doing so. They illustrate this method with an analysis of suburban location in the metropolitan area around New York City, focusing on two features of suburban communities-their homeownership level and median household income.

Cross-validation in regression and covariance structure-analysis - an overview

Abstract: This article gives an overview of cross-validation techniques in regression and covariance structure analysis. The method of cross-validation offers a means for checking the accuracy or reliability of results that were obtained by an exploratory analysis of the data. Cross-validation provides the possibility to select, from a set of alternative models, the model with the greatest predictive validity, that is, the model that cross-validates best. The disadvantage of cross-validation is that the data need to be split in two or more parts. This can be a serious problem when sample size is small. Various authors have therefore tried to find single sample criteria that provide the same kind of information as the cross-validation criteria but that do not require the use of a validation sample. Several of these criteria will be discussed, along with some results from studies comparing cross-validation and single sample criteria in covariance structure analysis.

Some New Covariance Structure Model Improvement Statistics

Abstract: Model modification in covariance structure analysis through reducing constraints can have an impact on the estimates of the maintained free parameters if the model is reevaluated. Three new statist...

Time-Aggregation Bias in Hazard-Rate Models with Covariates

Abstract: This article elaborates the results found in Petersen (1991) by discussing how to minimize time-aggregation bias in hazard-rate models with measured covariates. It first considers a model with a single categorical covariate with h categories. It derives analytically the bias of the estimator that assumes the durations are exactly measured, when the durations are in fact rounded up to the nearest integer. Second, the article conducts both large-and small-sample Monte Carlo studies for several estimators of the covariate effects in the presence of time aggregation. There are three central findings. (1) It is shown that the likelihood that adjusts for the grouped nature of the duration measures recaptures the parameters very well. (2) The estimator that assumes that the durations are exactly measured, when they in fact are rounded up to the nearest integer, is biased in several ways. (3) The estimator that uses the midpoint adjustment suffers from the same weaknesses as the estimator that rounds up to the ne...

Some Specification Tests for the Linear Regression Model

Abstract: A great deal of recent work in econometrics has focused on the development of tests to detect violations of the assumptions of ordinary least squares regression. These tests are referred to collectively as specification tests. This article evaluates some important and computationally convenient specification tests for the normal regression model as applied to cross-sectional data. Because these tests achieve their optimal properties in large samples, their size and power in finite samples are of great interest and are evaluated with Monte Carlo simulations. Although the authors' experiments showed a tendency toward overrejection in some tests, their results suggest that specific variations of the RESET and information matrix tests behave quite well even in small samples. They conclude by proposing a strategy for the sequential application of specification tests.

Dynamic Career Models and Inequality Research: A Reexamination of the Sørensen Model

Abstract: This article presents a reexamination of the Sorensen model. This model derives the pattern of individual careers from structural considerations. If longitudinal data on individual careers are available, Sorensen's model provides two methods to infer the underlying structural parameter. This structural parameter gives a useful measure for unequal career chances. An implementation of these methods, using firm data, shows, however, that they lead to contradictory conclusions; this is shown to be the result of some unrealistic assumptions Sorensen uses in his derivation. Some more realistic assumptions are suggested that produce reasonable results. Finally, it is shown that despite these modifications, the main conclusions of the Sorensen model are preserved. This seems to be promising for future work with this model.

Response Effects in a Network Survey

Abstract: A reverse record check study was conducted to measure the response effects in a network survey designed to locate a rare population subgroup. In this field experiment, diagnosed cancer patients and...