Showing papers in "Psychological Methods in 1999"
TL;DR: This paper reviewed the major design and analytical decisions that must be made when conducting exploratory factor analysis and notes that each of these decisions has important consequences for the obtained results, and the implications of these practices for psychological research are discussed.
Abstract: Despite the widespread use of exploratory factor analysis in psychological research, researchers often make questionable decisions when conducting these analyses. This article reviews the major design and analytical decisions that must be made when conducting a factor analysis and notes that each of these decisions has important consequences for the obtained results. Recommendations that have been made in the methodological literature are discussed. Analyses of 3 existing empirical data sets are used to illustrate how questionable decisions in conducting factor analyses can yield problematic results. The article presents a survey of 2 prominent journals that suggests that researchers routinely conduct analyses using such questionable methods. The implications of these practices for psychological research are discussed, and the reasons for current practices are reviewed.
7,590 citations
TL;DR: A fundamental misconception about this issue is that the minimum sample size required to obtain factor solutions that are adequately stable and that correspond closely to population factors is not the optimal sample size.
Abstract: The factor analysis literature includes a range of recommendations regarding the minimum sample size necessary to obtain factor solutions that are adequately stable and that correspond closely to population factors. A fundamental misconception about this issue is that the minimum sample size, or the
4,166 citations
TL;DR: Agroup-based method for identifying distinctive groups of individual trajectories within the population and for profiling the characteristics of group members is demonstrated.
Abstract: Carnegie Mellon UniversityA developmental trajectory describes the course of a behavior over age or time. Agroup-based method for identifying distinctive groups of individual trajectorieswithin the population and for profiling the characteristics of group members isdemonstrated. Such clusters might include groups of "increasers." "decreasers,"and "no changers." Suitably defined probability distributions are used to handle 3data types—count, binary, and psychometric scale data. Four capabilities are dem-onstrated: (a) the capability to identify rather than assume distinctive groups oftrajectories, (b) the capability to estimate the proportion of the population followingeach such trajectory group, (c) the capability to relate group membership probabil-ity to individual characteristics and circumstances, and (d) the capability to use thegroup membership probabilities for various other purposes such as creating profilesof group members.
2,163 citations
571 citations
TL;DR: In between-subje cts (BS) designs, different groups may be asked to make judgments on numerical rating scales as discussed by the authors, which can lead to strange conclusions: when different groups judge the subjective size of numbers, 9 is judged significantly larger than 221.
Abstract: In between-subje cts (BS) designs, different groups may be asked to make judgments on numerical rating scales. According to judgment theory, judgments obtained BS are not an ordinal scale of subjective value. This article illustrates how BS designs can lead to strange conclusions: When different groups judge the subjective size of numbers, 9 is judged significantly larger than 221. The theory is that 9 brings to mind a context of small numbers, among which 9 seems "average" or even "large"; however, 221 invokes a context of 3-digit numbers, among which 221 seems relatively "small." Within-subjects, however, judges would not have said 9 > 221. Implications of this problem and suggestions for dealing with it are discussed. The purpose of this article is to illustrate how between-subjects (BS) experiments, in which the dependent variable is a judgment, can lead to dubious conclusions. Although this point has been made previously (Birnbaum, 1974, 1982, 1992; Birnbaum & Mellers, 1983; Greenwald, 1976; Grice, 1966), the implications of this thesis may not yet be fully appreciated by researchers. This article uses a simple example lo illustrate how difficult it is to compare judgments between subjects. When different groups of people judge a stimulus, the response by a given person on a specific occasion is theorized to be a function of subjective value: R(i,k) = Jk(s,)
196 citations
190 citations
TL;DR: In this article, the authors present the intrinsic and extrinsic justifications for studying famous persons, and the main methodological issues concerning sampling, measurement, and analysis are discussed, as well as the future prospects of significant samples in psychological research are briefly examined.
Abstract: Psychologists occasionally study eminent individuals, such as Nobel laureates, U.S. presidents, Olympic athletes, chess grandmasters, movie stars, and even distinguished psychologists. Studies using such significant samples may be differentiated along 7 distinct dimensions: qualitative versus quantitative, single versus multiple case, nomothetic versus idiographic, confirmatory versus exploratory, crosssectional versus longitudinal, micro versus macro analytical units, and direct versus indirect assessments. However, the vast majority of psychological inquiries may be clustered into just 4 types: historiometric, psychometric, psychobiographical, and comparative. After presenting the intrinsic and extrinsic justifications for studying famous persons, the main methodological issues concerning sampling, measurement, and analysis are discussed. The future prospects of significant samples in psychological research are then briefly examined.
134 citations
114 citations
103 citations
78 citations
TL;DR: In this paper, a new model of change is proposed, based on the assumption that cognitive and behavioral processes of change basically follow inverse-U-shaped patterns of variation as smokers move toward effective change: each process is first increasingly used, up to a maximum value, and then decreases.
Abstract: A new model of change is proposed, based on the assumption that cognitive and behavioral processes of change basically follow inverse-U-shaped patterns of variation as smokers move toward effective change: Each process is first increasingly used, up to a maximum value, and then decreases. It is argued that such a model of data is properly dealt with by unfolding models specially designed for those cases. A theoretical foundation for an unfolding model of change is proposed, based on probabilistic reasoning first developed by D. Andrich and G. Luo (1993). An illustrative analysis on responses of 140 French smokers to C. C. DiClemente and J. O. Prochaska's (1985) Processes of Change Questionnaire is presented, which yields a very satisfactory unidimensional solution, along which items' locations are in convergence with previous longitudinal studies in the stage-of-change tradition and smokers' locations appear to be a good predictor of actual quitting. Numerous latent trait models for the measurement of attitudes and cognitive abilities have been proposed (Andrich, 1978b; Bock, 1972; Lord, 1952; Masters, 1982; Muraki, 1992; Samejima, 1969) that have expanded and sophisticated probabilistic response models initially proposed by Rasch (1960) in a tradition of psychological measurement that could be traced to Thurstone's (1927, 1928) seminal works. Extensions in the use of these models from structural traits measurement to longitudinal data analysis have been proposed (Fischer, 1989; Fischer & Parzer, 1991). In the structural equation modeling tradition, latent growth models are also available for the analysis of change (Duncan, Duncan, & Stoolmiller, 1994; Raykov, 1994). Those approaches, however, either assume a cumulative latent evolutionary process or require that repeated measures be available to estimate the (potentially nonlinear) growth function.
TL;DR: The authors provided a compelling example of an incorrect conclusion drawn from a non-random sample: H. C. Lomard's (1835) mortality data, augmented by a second example (A. W. Wald, 1980) that shows how modeling the selection mechanism can correct for the bias introduced by nonsampling errors.
Abstract: Nonsampling errors are subtle, and strategies for dealing with them are not particularly well known within psychology. This article provides a compelling example of an incorrect conclusion drawn from a nonrandom sample: H. C. Lomard's (1835) mortality data. This example is augmented by a second example (A. Wald, 1980) that shows how modeling the selection mechanism can correct for the bias introduced by nonsampling errors. These 2 examples are then connected to modern statistical methods that through the method of multiple imputation allow researchers to assess uncertainty in observational studies. The APA's task force on Statistical Inference has received comments and suggestions from interested parties throughout the entire time I have served on it. These comments have always been treated by the task force with careful attention. In the most recent batch was a one-page missive from John Tukey containing seven suggestions. In the course of my professional life I have made many errors, but happily, ignoring statistical advice from John Tukey is not one of them. Tukey's fifth suggestion, in its entirety, is, "non-.ampling errors deserve greater attention, especially when randomization is absent. The formal statistical analysis treats only some of the uncertainties" (J. W. Tukey, personal communication, June 16, 1997). Indeed, but nonsampling errors are subtle, and strategies for dealing with them are not particularly well known within psychology. Thus, I think it would be worthwhile to provide a particularly interesting illustration of one and point the way toward alternative methodologies for interested readers.
TL;DR: In this article, the authors discuss the concepts that underlie the solutions for 1-way and 2-way ANOVAs with overparameterized models and illustrate how these models allow one to evaluate the research hypotheses.
Abstract: Analyses of variance (ANOVA) with the general linear model (GLM) in many standard statistical packages use an Overparameter ized model, a model unfamiliar to most behavioral science researchers. Estimates and significance tests with GLM procedures are calculated by computing generalized inverses and estimates of estimable functions. Using simple examples, the authors discuss the concepts that underlie the solutions for 1-way and 2-way ANOVAs with Overparameterized models and illustrate how these models allow one to evaluate the research hypotheses. The authors also extend the discussion of Overparameterized models to a more general modeling approach than GLM, the general linear mixed model. Many students and researchers in the behavioral -ciences routinely conduct analyses of variance