The Impact of Inappropriate Modeling of Cross-Classified Data Structures

Cross-classified random effects modeling (CCREM) is used to model multilevel data from nonhierarchical contexts. These models are widely discussed but infrequently used in social science research. Because little research exists assessing when it is necessary to use CCREM, 2 studies were conducted. A real data set with a cross-classified structure was analyzed by comparing parameter estimates when ignoring versus modeling the cross-classified data structure. A follow-up simulation study investigated potential factors affecting the need to use CCREM. Results indicated that when the structure is ignored, fixed-effect estimates were unaffected, but standard error estimates associated with the variables modeled incorrectly were biased. Estimates of the variance components also displayed bias, which was related to several study factors.     

The Consequences of Ignoring Variability in Measurement Occasions Within Data Collection Waves in Latent Growth Models

In longitudinal data collection, it is common that each wave of collection spans several months. However, researchers using latent growth models commonly ignore variability in data collection occasions within a wave. In this study, we investigated the consequences of ignoring within-wave variability in measurement occasions using a Monte Carlo simulation and an empirical study. The results of the simulation study showed that ignoring heterogeneity resulted in biased estimates for some parameters, especially when heterogeneity was large and assessment dates had a skewed distribution. Models constructed on person-specific time points yielded precise estimates and more adequate model fit. In the empirical study, we demonstrated different time coding strategies with a subsample taken from Early Childhood Longitudinal Study Kindergarten Cohort.     

Ignoring Clustering in Confirmatory Factor Analysis: Some Consequences for Model Fit and Standardized Parameter Estimates

In many situations, researchers collect multilevel (clustered or nested) data yet analyze the data either ignoring the clustering (disaggregation) or averaging the micro-level units within each cluster and analyzing the aggregated data at the macro level (aggregation). In this study we investigate the effects of ignoring the nested nature of data in confirmatory factor analysis (CFA). The bias incurred by ignoring clustering is examined in terms of model fit and standardized parameter estimates, which are usually of interest to researchers who use CFA. We find that the disaggregation approach increases model misfit, especially when the intraclass correlation (ICC) is high, whereas the aggregation approach results in accurate detection of model misfit in the macro level. Standardized parameter estimates from the disaggregation and aggregation approaches are deviated toward the values of the macro- and micro-level standardized parameter estimates, respectively. The degree of deviation depends on ICC and cluster size, particularly for the aggregation method. The standard errors of standardized parameter estimates from the disaggregation approach depend on the macro-level item communalities. Those from the aggregation approach underestimate the standard errors in multilevel CFA (MCFA), especially when ICC is low. Thus, we conclude that MCFA or an alternative approach should be used if possible.     

Estimation of a Latent Variable Regression Growth Curve Model for Individuals Cross-Classified by Clusters

The cross-classified multiple membership latent variable regression (CCMM-LVR) model is a recent extension to the three-level latent variable regression (HM3-LVR) model which can be utilized for longitudinal data that contains individuals who changed clusters over time (for instance, student mobility across schools). The HM3-LVR model can include the initial status on growth effect as varying across those clusters and allows testing of more flexible hypotheses about the influence of initial status on growth and of factors that might impact that relationship, but only in the presence of pure clustering of participants within higher-level units. This Monte Carlo study was conducted to evaluate model estimation under a variety of conditions and to measure the impact of ignoring cross-classified data when estimating the incorrectly specified HM3-LVR model in a scenario in which true values for parameters are known. Furthermore, results from a real-data analysis were used to inform the design of the simulation. Overall, it would be recommended for researchers to utilize the CCMM-LVR model over the HM3-LVR model when individuals are cross-classified, and to use a bare minimum of more than 100 clustering units in order to avoid overestimation of the level-3 variance component estimates.     

Ignoring biased feedback: Membership in a stigmatized group as a moderator of mnemic neglect

A personal history of being the target of biased negative evaluation may lead individuals to habitually divert attention from negative feedback when it is possible to do so. Two studies tested for the first time the hypothesis that people belonging to a stigmatized group (Black students on a predominantly White campus) will, relative to non-stigmatized people, be more likely to engage in mnemic neglect—that is, they will reveal a greater tendency to insulate themselves from the effects of negative self-relevant feedback by means of motivated forgetting. The results of Study 1 supported that hypothesis. In Study 2, priming the concept of egalitarianism reduced the tendency of Black participants to engage in higher levels of mnemic neglect, consistent with the idea that the higher observed levels of mnemic neglect among stigmatized individuals derives from expecting biased, discriminatory responses from other people.     

Crossed aphasia in Chinese: a clinical survey.

According to our clinical observations from various aspects of stroke patients, such as the total incidence of aphasia, the incidence of aphasia after left brain damage of the dextrals, the aphasia that occurs in patients without hemiplegia, and the types of aphasia, a much higher incidence of crossed aphasia is seen among the stroke patients of the Han (the largest ethnic group in China) as compared with the Uighur-Kazaks (U-K) in China and the Occidentals documented in the literature. Motor aphasia is most common and pure sensory or posterior aphasia is rarely seen in Han patients. The distinct features of the Chinese language is a possible explanation for this difference. We suspect that language function of the Han is not localized in the left brain but in the right or both hemispheres. There is no definite Wernicke's area in the left brain of the Chinese people and the neural pathway of the language function in the brain of the Chinese people is not similar to people who speak phonetic languages. Consequently the universal applicability of the theories of cerebral laterality of the language function and dominant hemisphere established by Dax and Broca are questioned in this paper.     

Crossed apraxia of speech: a case report

The present study reports on the first case of crossed apraxia of speech (CAS) in a 69-year-old right-handed female (SE). The possibility of occurrence of apraxia of speech (AOS) following right hemisphere lesion is discussed in the context of known occurrences of ideomotor apraxias and acquired neurogenic stuttering in several cases with right hemisphere lesion. A current hypothesis on AOS-the dual route speech encoding (DRSE) hypothesis-and predictions based on DRSE were utilized to explore the nature of CAS in SE. One prediction based on the DRSE hypothesis is that there should be no difference in the frequency of occurrence of apraxic errors on words and non-words. This prediction was tested using a repetition task. The experimental stimuli included a list of minimal pairs that signaled voice-voiceless contrasts in words and non-words. Minimal-pair stimuli were presented orally, one at a time. SE's responses were recorded using audio and videotapes. Results indicate that SE's responses were characterized by numerous voicing errors. Most importantly, production of real word minimal pairs was superior to that of non-word minimal pairs. Implications of these results for the DRSE hypothesis are discussed with regard to currently developing perspectives on AOS.     

SORTO: Factor Analyzing Q Sorts of Kelly's Personal Construct Productions

A procedure for evaluating personality is described. Conventional and transposed factor analyses are made from Q sort data describing the important people in the subject's life in terms of his own constructs (á la Kelly) as variables. The scoring procedure produces construct-factors and people-factors. Sorts from a subject illustrate the method. Cross-cultural applications are possible since the translation of personal constructs is not essential. Simulation of relationships to others, SORTO, combines Kelly's (1955) personal constructs with Stephenson's (1953) Q sort procedure. A large amount of personal data is factor analyzed by the computer to reveal the main idiosyncratic features of a subject's perceptions of his relationships to others. Maximum output from the analysis occurs when the nature of personal constructs employed is supplied as input to the analysis.     

The Factor Structure of Cognitive Functioning in Cognitively Healthy Participants: a Meta-Analysis and Meta-Analysis of Individual Participant Data

Neuropsychology Review - Many neuropsychologists are of the opinion that the multitude of cognitive tests may be grouped into a much smaller number of cognitive domains. However, there is little...     

Tactless scientists: Ignoring touch in the study of joint attention

Since the 1970s, researchers have focused on visual joint attention as a way to observe and operationalize joint attention (JA). I will argue that this methodological choice has neglected other modalities and as a consequence might be missing important elements in the account of the development of JA and the evolutionary history of JA. I argue that by including other modes of interaction, such as touch, we open the possibility of finding that non-human primates and younger human infants engage in basic forms of JA. Moreover, I argue that touch, as a proximal mode of perception, is the ontogenetic precursor of social communication.     

Confirmatory Factor Analysis of Multitrait-Multimethod Data: The Construct Validation of Multidimensional Self-Concept Responses

ABSTRACT The multitrait-multimethod (MTMM) design is frequently used to test construct validity and is particularly appropriate for evaluating multidimensional instruments in personality research Despite its popularity there are important problems with both the traditional and confirmatory factor analysis (CFA) approaches to MTMM data In previous research (1989a, 1989b) I described a new CFA approach that I claimed to be relatively immune to many of these problems This approach is applied here to test the construct validity of preadolescent responses to three multidimensional self-concept instruments The substantive contribution of the present investigation is to demonstrate good support for the construct validity of two of the three instruments The methodological contributions are to further substantiate my claims about the new CFA approaches and to demonstrate this approach in a substantively meaningful context that has broad applicability to other personality research     

群体交叉分类效应的代数模型及其潜在加工过程

交叉分类被认为是一种能够有效减少群际偏见的方法。交叉分类效应的代数模型主要包括平均模型、相加模型和等价模型,各个模型都有自己成立的证据,其中支持相加模型的研究证据最多。尽管如此,交叉分类效应的代数模型有很明显的局限,即要求所有进行交叉的类别维度权重相等,这与真实社会情境是相矛盾的。交叉分类的潜在加工过程主要有基于认知角度的类别差异模型和基于动机角度的社会认同两种比较成熟的理论,前者又分为类别差异消除模型和类别差异减少模型。     

Analyzing distances in a frontal plane

In an exploration of the process whereby horizontal and vertical components are extracted from distances between pairs of points, participants made speeded absolute distance judgments—deciding whether horizontal distances between pairs of points in a frontal plane exceeded a criterion distance. Judgments reflected the horizontal and vertical distances between the points. Three accounts of the results were considered. (1) Only the overall distance between two points was directly available to judgment processes; the horizontal or vertical distance between the points was available only to the extent that the horizontal and vertical positions of the points were differentially weighted prior to the assessment of overall distance. (2) The perceptual effects of positions on the horizontal and vertical dimensions were collapsed onto a composite dimension. (3) The decision criterion for the distance judgment considered both the horizontal and vertical distances between the points. In support of the overall distance account, (1) performance in the distance judgment task was facilitated by repetition of the same overall distance from trial to trial, but not by repetition of the distance on the composite dimension or by repetition of horizontal or vertical distance; (2) differential weighting that the overall distance account predicted for the absolute distance judgments was reflected in concurrent relative distance judgments pitting horizontal against vertical distance, counter to the composite dimension account, which sees the horizontal and vertical dimensions as collapsing onto the composite dimension in balanced, symmetrical fashion, and counter the decisional account, given that no criterion is required for the relative judgment.     

探索性因素分析因子抽取方法的比较

探索性因素分析决定因子抽取的方法主要有Bartlett法、K1原则、碎石检验法、Aaker原则、PA、MAP等六种,通过对样本1的395名大学生的学习过程问卷调查获得真实数据,运用这六种方法进行因素分析分别抽取7、4、2、4、3、2个因子;应用样本2的383名大学生的问卷调查数据进行验证性因素分析,结果显示,碎石检验方法与MAP方法抽取的二因素模型更理想。研究表明,因子抽取需兼顾“简约性原则”与“完备性原则”,同时要根据一定的理论建构、专业知识和经验来决定因子数。     

Comparison of Modern Methods for Analyzing Repeated Measures Data With Missing Values

Missing data are a pervasive problem in many psychological applications in the real world. In this article we study the impact of dropout on the operational characteristics of several approaches that can be easily implemented with commercially available software. These approaches include the covariance pattern model based on an unstructured covariance matrix (CPM-U) and the true covariance matrix (CPM-T), multiple imputation-based generalized estimating equations (MI-GEE), and weighted generalized estimating equations (WGEE). Under the missing at random mechanism, the MI-GEE approach was always robust. The CPM-T and CPM-U methods were also able to control the error rates provided that certain minimum sample size requirements were met, whereas the WGEE was more prone to inflated error rates. In contrast, under the missing not at random mechanism, all evaluated approaches were generally invalid. Our results also indicate that the CPM methods were more powerful than the MI-GEE and WGEE methods and their superiority was often substantial. Furthermore, we note that little or no power was sacrificed by using CPM-U method in place of CPM-T, although both methods have less power in situations where some participants have incomplete data. Some aspects of the CPM-U and MI-GEE methods are illustrated using real data from 2 previously published data sets. The first data set comes from a randomized study of AIDS patients with advanced immune suppression, the second from a cohort of patients with schizotypal personality disorder enrolled in a prevention program for psychosis.     

The Treatment of Relevant and Irrelevant Outgroups in Minimal Group Situations With Crossed Categorizations

Abstract

Among a sample of Belgian high school boys, the author attempted to determine on the basis of social identity theory (H. Tajfel & J. C. Turner, 1979) whether an outgroup's relevancy in intergroup comparisons influences the amount of intergroup discrimination. Relevancy was manipulated by the use of 2 trivial categorization dimensions that were orthogonally crossed. In 1 of the crossed categorization conditions, this procedure resulted in the formation of 4 groups, whereas in the 2nd condition, only 2 of these groups were effectively formed. Nevertheless, the participants in both conditions were instructed to evaluate 4 groups–either 4 actual groups (1st condition) or 2 actual and 2 hypothetical groups (2nd condition). In both conditions, the intergroup evaluations exhibited the same pattern–that is, they did not differentiate their ingroups from the partly overlapping groups but were clearly biased against the double outgroup.     

Controlling Correlational Bias via Confirmatory Factor Analysis of MTMM Data

Confirmatory factor analysis of multitrait-multimethod (MTMM) data has proven to be a useful tool for assessing convergent and discriminant validity. However, researchers have not made full use of the results of MTMM analyses in examining the relationship between MTMM factors and variables outside the MTMM. Often, researchers simply average the various measures of each trait. Alternatively, they estimate LISREL MTMM models, but estimate only relationships between MTMM traits and the outside variables. In the present article, we show that these two approaches to analyzing data outside the MTMM produce equally highly biased parameter estimates when the actual correlations between MTMM method factors and the outside variables are substantial. An algebraic explanation and a simulated data illustration are given for the bias due to misspecification. Also, the problem is illustrated with a brief empirical example. Implications for applied research are discussed.     

Ignoring 'brutal' will make 'numid' more pleasant but 'uyuvu' more unpleasant: The role of a priori pleasantness of unfamiliar stimuli in affective priming tasks

Based on assumptions borrowed from negative priming research, in two experiments an evaluative decision to a clearly valenced target word that was flanked by a positive or negative distractor (i.e., the “affective priming” technique in the tradition of of Fazio, Sanbonmatsu, Powell, & Kardes, 1986) was followed by a pleasantness rating of an unfamiliar, senseless string of letters. Of main interest was whether an inhibition of the distractor valence affects the rating. Unexpectedly, it was found that the effect depended on subtle differences in the a priori pleasantness of the letter strings. For (relatively) less pleasant sounding strings, the rating was congruent to the prime whereas it was incongruent for (relatively) more pleasant sounding strings. In Experiment 3, the rating of the letter strings was preceded by a masked positive or negative prime only. The same pattern of congruence and incongruence effects emerged. The results are related to a matching account of negative priming.     

Learning Impacts of a Digital Augmentation in a Science Museum

ABSTRACT

This study investigated the effects of using digital augmentation to enhance an exhibit device to influence conceptual understanding about a science topic in a science museum setting. In particular, the study considered how students in Grades 6–8 engaged with the device that was available in both augmented and nonaugmented (control) conditions. Results show increased cognitive (critical thinking) skills when the digital augmentation was present that we hypothesize led to increased conceptual gains. We illustrate how this research contributes to three important areas of need identified in informal science literature: the need for evidence of conceptual and cognitive gains; the need for understanding how digital platforms improve the learning experience; and the need to demonstrate how designed interactive devices may impact higher order skills such as critical thinking and theorizing.