Self-presentation styles and Problematic use of Internet communicative services: The role of the concerns over behavioral displays of imperfection

The current study had the dual purpose of identifying consequences associated with perfectionistic self-presentation (PSP) and evaluating these factors as mediator of the proposed link between PSP and Problematic use of Internet communicative services (GPIU). The present study hypothesized that a self-presentation style characterized by the need to avoid displaying imperfections is associated with GPIU because communicating online rather than in person affords greater control through the reduction of non-verbal cues and the greater temporal flexibility. A sample of 200 university student participants completed measures assessing PSP, GPIU, and perceived controllable aspects of behaviors associated with relying on computer mediated communications (i.e. managing nonverbal displays and buying more time before having to respond). Structural equation modeling confirmed that those who systematically try to avoid revealing their supposed “less than perfect” behaviors or performance place great emphasis on the reduction of nonverbal cues and the temporal flexibility offered by the computer mediated interactions, which, in turn predicts GPIU levels. The findings are discussed in terms of their implications for GPIU and for the perfectionism social disconnection model.     

Accuracy, Scope, and Flexibility of Models

Traditionally, models are compared on the basis of their accuracy, their scope, and their simplicity. Simplicity is often represented by parameter counts; the fewer the parameters, the simpler the model. Arguments are presented here suggesting that simplicity has little place in discussions of modeling; instead, the concept of flexibility should be substituted. When comparing two models one should be wary of the possibility of their differential flexibility. Several methods for assessing relative flexibility are possible, as represented in this special issue of the Journal of Mathematical Psychology. Here, the method of cross-validation is applied in the comparison of two models, a linear integration model (LIM) and the fuzzy-logical model of perception (FLMP), in the fitting of 44 data sets concerning the perception of layout seen among three panels with the presence or absence of four sources of information for depth. Prior to cross-validation the two models performed about equally well; after cross-validation LIM was statistically superior to FLMP, but the overall pattern of fits remained nearly the same for both models. Copyright 2000 Academic Press.     

Using SAS PROC CALIS to fit Level-1 error covariance structures of latent growth models

In the present article, we demonstrates the use of SAS PROC CALIS to fit various types of Level-1 error covariance structures of latent growth models (LGM). Advantages of the SEM approach, on which PROC CALIS is based, include the capabilities of modeling the change over time for latent constructs, measured by multiple indicators; embedding LGM into a larger latent variable model; incorporating measurement models for latent predictors; and better assessing model fit and the flexibility in specifying error covariance structures. The strength of PROC CALIS is always accompanied with technical coding work, which needs to be specifically addressed. We provide a tutorial on the SAS syntax for modeling the growth of a manifest variable and the growth of a latent construct, focusing the documentation on the specification of Level-1 error covariance structures. Illustrations are conducted with the data generated from two given latent growth models. The coding provided is helpful when the growth model has been well determined and the Level-1 error covariance structure is to be identified.     

Selectivity, scope, and simplicity of models: a lesson from fitting judgments of perceived depth.

When comparing psychological models a researcher should assess their relative selectivity, scope, and simplicity. The third of these considerations can be measured by the models' parameter counts or equation length, the second by their ability to fit random data, and the first by their differential ability to fit patterned data over random data. These conclusions are based on exploration of integration models reflecting depth judgments. Replication of Massaro's (1988a) results revealed an additive model (Bruno & Cutting, 1988), and Massaro's fuzzy-logical model of perception (FLMP) fit data equally well, but further exploration showed that the FLMP fit random data better. The FLMP's successes may reflect not its sensitivity in capturing psychological process but its scope in fitting any data and its complexity as measured by equation length.     

A Structural Approach to External and Internal Person-Team Fit

This article develops a structurally based, integrated theory of person-organisation fit. This approach to fit recognises the need for achieving both a good external fit between the organisation's structure and its environment, as well as a good internal fit between the organisation's structure and its own members. The three most critical aspects of organisational structure within this theory are departmentation, centralisation, and adaptability as these are manifested at the team level. Structural contingency theory is used to derive predictions regarding how these types of structural characteristics interact with the nature of the environment (in terms of predictability and stability) to determine external fit (because of their influence on efficiency, flexibility, and responsiveness). The five factor model of personality is used to derive predictions regarding how these same types of structures interact with individual differences to determine internal fit (because of their influence on role requirements such as scope, interdependence, and innovation). Both types of fit are critical to organisational performance, because in many instances, the presence of a poor fit on one dimension may neutralise the otherwise positive effects of a good fit on the other dimension.     

Nonlinear Structured Growth Mixture Models in Mplus and OpenMx

Growth mixture models (GMMs; B. O. Muthén & Muthén, 2000; B. O. Muthén & Shedden, 1999) are a combination of latent curve models (LCMs) and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. GMMs are often fit with linear, latent basis, multiphase, or polynomial change models because of their common use, flexibility in modeling many types of change patterns, the availability of statistical programs to fit such models, and the ease of programming. In this article, we present additional ways of modeling nonlinear change patterns with GMMs. Specifically, we show how LCMs that follow specific nonlinear functions can be extended to examine the presence of multiple latent classes using the Mplus and OpenMx computer programs. These models are fit to longitudinal reading data from the Early Childhood Longitudinal Study–Kindergarten Cohort to illustrate their use.     

On the adequacy of current empirical evaluations of formal models of categorization

Categorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of noncomplementary models with little consensus on the relative adequacy of these accounts. Progress in assessing the relative adequacy of formal categorization models has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus).     

Model discrimination through adaptive experimentation

An ideal experiment is one in which data collection is efficient and the results are maximally informative. This standard can be difficult to achieve because of uncertainties about the consequences of design decisions. We demonstrate the success of a Bayesian adaptive method (adaptive design optimization, ADO) in optimizing design decisions when comparing models of the time course of forgetting. Across a series of testing stages, ADO intelligently adapts the retention interval in order to maximally discriminate power and exponential models. Compared with two different control (non-adaptive) methods, ADO distinguishes the models decisively, with the results unambiguously favoring the power model. Analyses suggest that ADO's success is due in part to its flexibility in adjusting to individual differences. This implementation of ADO serves as an important first step in assessing its applicability and usefulness to psychology.     

The Role of Person‐Culture Fit in Chinese Students' Cultural Adjustment in the United States: A Galileo Mental Model Approach

This article used a Galileo multidimensional scaling model to explain how intercultural communication affected sojourners' wellbeing in their host culture through the convergence of the mental models between sojourners and host nationals with whom the sojourners communicated. Participants were 126 sojourning Chinese in the United States and 30 associated domestic Americans. Data were collected shortly after the Chinese sojourners arrived in the United States and again 3 months later. A measure of person‐culture fit was generated by comparing each Chinese sojourner's mental model with the aggregate mental model of the associated domestic Americans. Results showed that the amount of intercultural communication had a significant effect on person‐culture fit, in turn affecting sojourners' adaptation outcomes. Theoretical and practical implications of the results are discussed.     

Multi-disclinational description of pentagonal particles with subsurface layer free of twin boundaries

We present the results on the modelling of structural changes in pentagonal small particles (PSPs) during their growth. We prove that after a certain critical size it becomes energetically favourable for a PSP to form a subsurface layer free of twin boundaries (TBs), which are only typical structural elements for smaller size PSPs. In this layer, the low-angle dislocation boundaries (DBs) are formed. Our calculations of the energy stored in the transformed PSP are based on the disclination model of a PSP, in which the TB junctions, as well as TB–DB junctions are treated as wedge disclinations.     

Bayesian item fit analysis for unidimensional item response theory models

Assessing item fit for unidimensional item response theory models for dichotomous items has always been an issue of enormous interest, but there exists no unanimously agreed item fit diagnostic for these models, and hence there is room for further investigation of the area. This paper employs the posterior predictive model‐checking method, a popular Bayesian model‐checking tool, to examine item fit for the above‐mentioned models. An item fit plot, comparing the observed and predicted proportion‐correct scores of examinees with different raw scores, is suggested. This paper also suggests how to obtain posterior predictive p‐values (which are natural Bayesian p‐values) for the item fit statistics of Orlando and Thissen that summarize numerically the information in the above‐mentioned item fit plots. A number of simulation studies and a real data application demonstrate the effectiveness of the suggested item fit diagnostics. The suggested techniques seem to have adequate power and reasonable Type I error rate, and psychometricians will find them promising.     

Flexibility versus generalizability in model selection

Which quantitative method should be used to choose among competing mathematical models of cognition? Massaro, Cohen, Campbell, and Rodriguez (2001) favor root mean squared deviation (RMSD), choosing the model that provides the best fit to the data. Their simulation results appear to legitimize its use for comparing two models of information integration because it performed just as well as Bayesian model selection (BMS), which had previously been shown by Myung and Pitt (1997) to be a superior alternative selection method because it considers a model's complexity in addition to its fit. In the present study, after contrasting the theoretical approaches to model selection espoused by Massaro et al. and Myung and Pitt, we discuss the cause of the inconsistencies by expanding on the simulations of Massaro et al. Findings demonstrate that the results from model recovery simulations can be misleading if they are not interpreted relative to the data on which they were evaluated, and that BMS is a more robust selection method.     

Everything you always wanted to know about SEM (structural equations modeling) but were afraid to ask

This article is intended to serve as a primer for structural equations models for the behavioral researcher. The technique is not mysterious—it is a natural extension of factor analysis and regression. The measurement part of a structural equations model is essentially a confirmatory factor analysis, and the structural part of the model is like a regression but vastly more flexible in the types of theoretical models that may be tested. The models and notation are introduced and the syntax is provided to replicate the analyses in the paper. Part II of this article will appear in the next issue of the Journal of Consumer Psychology, and it covers advanced issues, including fit indices, sample size, moderators, longitudinal data, mediation, and so forth.     

Evaluating models of remember-know judgments: Complexity, mimicry, and discriminability

Remember-know judgments provide additional information in recognition memory tests, but the nature of this information and the attendant decision process are in dispute. Competing models have proposed that remember judgments reflect a sum of familiarity and recollective information (the one-dimensional model), are based on a difference between these strengths (STREAK), or are purely recollective (the dual-process model). A choice among these accounts is sometimes made by comparing the precision of their fits to data, but this strategy may be muddied by differences in model complexity: Some models that appear to provide good fits may simply be better able to mimic the data produced by other models. To evaluate this possibility, we simulated data with each of the models in each of three popular remember-know paradigms, then fit those data to each of the models. We found that the one-dimensional model is generally less complex than the others, but despite this handicap, it dominates the others as the best-fitting model. For both reasons, the one-dimensional model should be preferred. In addition, we found that some empirical paradigms are ill-suited for distinguishing among models. For example, data collected by soliciting remember/know/new judgments--that is, the trinary task--provide a particularly weak ground for distinguishing models. Additional tables and figures may be downloaded from the Psychonomic Society's Archive of Norms, Stimuli, and Data, at www.psychonomic.org/archive.     

A generalized,likelihood-free method for posterior estimation

Recent advancements in Bayesian modeling have allowed for likelihood-free posterior estimation. Such estimation techniques are crucial to the understanding of simulation-based models, whose likelihood functions may be difficult or even impossible to derive. However, current approaches are limited by their dependence on sufficient statistics and/or tolerance thresholds. In this article, we provide a new approach that requires no summary statistics, error terms, or thresholds and is generalizable to all models in psychology that can be simulated. We use our algorithm to fit a variety of cognitive models with known likelihood functions to ensure the accuracy of our approach. We then apply our method to two real-world examples to illustrate the types of complex problems our method solves. In the first example, we fit an error-correcting criterion model of signal detection, whose criterion dynamically adjusts after every trial. We then fit two models of choice response time to experimental data: the linear ballistic accumulator model, which has a known likelihood, and the leaky competing accumulator model, whose likelihood is intractable. The estimated posterior distributions of the two models allow for direct parameter interpretation and model comparison by means of conventional Bayesian statistics—a feat that was not previously possible.     

A test of the regulatory fit hypothesis in perceptual classification learning

This article builds a bridge between research on regulatory focus in motivation and classification learning. It tests the hypothesis that a fit between the situational regulatory focus and the reward structure of the task leads to greater cognitive flexibility than does a mismatch between situational focus and the reward structure and that the fit between the regulatory-focus-induced processing characteristics and the nature of the environment influences performance. In Experiment 1, we used a classification task for which cognitive flexibility should be advantageous and examined both gains (Experiment 1A) and losses (Experiment 1B) reward structures. In Experiments 2 and 3, we used a classification task for which cognitive flexibility should be disadvantageous. In Experiment 2, we used a gains reward structure, and in Experiment 3, we used a losses reward structure. As was predicted, when cognitive flexibility was advantageous, the participants in a regulatory fit showed faster learning and more quickly shifted toward the optimal response strategy. Also as was predicted, when cognitive flexibility was disadvantageous, the participants in a regulatory mismatch showed faster learning and more quickly shifted toward the optimal response strategy. Implications for current theories of motivation and classification learning are discussed.     

Comparative Study Between the Theory of Planned Behavior and the Value–Belief–Norm Model Regarding the Environment,on Spanish Housewives' Recycling Behavior

Environmental behaviors have been analyzed from different theoretical models. The theory of planned behavior emphasizes the attitudinal aspects associated with behavior, while the value–belief–norm model regarding the environment focuses on the importance of moral components when setting environmental behaviors in motion. The objective of this study was to analyze both models, comparing both their degree of fit and their predictive power regarding recycling behavior. To do so, we used a sample made up of 154 Spanish housewives. The results indicated that despite the fact that the theory of planned behavior is a general model for predicting and explaining behavior, it has a greater degree of fit and greater capacity to predict recycling behavior than the value–belief–norm model regarding the environment.     

Changing affordances in stair climbing: the perception of maximum climbability in young and older adults.

This experiment extended Warren's leg-length model by investigating the relevance of leg strength and joint flexibility on perceptual judgments of climbability. From a set of 8 stairs (riser heights: 38-91 cm), 24 older and 24 young adults were asked to identify the highest stair they could climb without using their hands or knees. Ss then attempted to climb the selected stair. Tall and short young observers perceived similar action boundaries despite leg-length differences. Tall and short older adults had divergent action boundaries when a single-scale leg-length model was applied. A regression model that used flexibility and leg-strength measurements provided a better fit of the older adult data, indicating that models applying functional (kinematic and kinetic) criteria might be useful in describing lawful relationships between organisms and the environment.