概化理论方差分量估计的跨分布分析

方差分量估计是进行概化理论分析的关键。采用MonteCarlo模拟技术,探讨心理与教育测量数据分布对概化理论各种方法估计方差分量的影响。数据分布包括正态、二项和多项分布,估计方法包括Traditional、Jackknife、Bootstrap和MCMC方法。结果表明:(1)Traditional方法估计正态分布和多项分布数据的方差分量相对较好,估计二项分布数据需要校正,Jackknife方法准确地估计了三种分布数据的方差分量,校正的Bootstrap方法和有先验信息的MCMC方法(MCMCinf)估计三种分布数据的方差分量结果较好;(2)心理与教育测量数据分布对四种方法估计概化理论方差分量有影响,数据分布制约着各种方差分量估计方法性能的发挥,需要加以区分地使用。     

MISSING DATA: A CONCEPTUAL REVIEW FOR APPLIED PSYCHOLOGISTS

There has been conspicuously little research concerning missing data problems in the applied psychology literature. Fortunately, other fields have begun to investigate this issue. These include survey research, marketing, statistics, economics, and biometrics. A review of this literature suggests several trends for applied psychologists. For example, listwise deletion of data is often the least accurate technique to deal with missing data. Other methods for estimating missing data scores may be more accurate and preserve more data for investigators to analyze. Further, the literature reveals that the amount of missing data and the reasons for deletion of data impact how investigators should handle the problem. Finally, there is a great need for more investigation of strategies for dealing with missing data, especially when data are missing in nonrandom or systematic patterns.     

SEM with Missing Data and Unknown Population Distributions Using Two-Stage ML: Theory and Its Application

This article provides the theory and application of the 2-stage maximum likelihood (ML) procedure for structural equation modeling (SEM) with missing data. The validity of this procedure does not require the assumption of a normally distributed population. When the population is normally distributed and all missing data are missing at random (MAR), the direct ML procedure is nearly optimal for SEM with missing data. When missing data mechanisms are unknown, including auxiliary variables in the analysis will make the missing data mechanism more likely to be MAR. It is much easier to include auxiliary variables in the 2-stage ML than in the direct ML. Based on most recent developments for missing data with an unknown population distribution, the article first provides the least technical material on why the normal distribution-based ML generates consistent parameter estimates when the missing data mechanism is MAR. The article also provides sufficient conditions for the 2-stage ML to be a valid statistical procedure in the general case. For the application of the 2-stage ML, an SAS IML program is given to perform the first-stage analysis and EQS codes are provided to perform the second-stage analysis. An example with open- and closed-book examination data is used to illustrate the application of the provided programs. One aim is for quantitative graduate students/applied psychometricians to understand the technical details for missing data analysis. Another aim is for applied researchers to use the method properly.     

How honest are the signals? A protocol for validating wearable sensors

There is growing interest among organizational researchers in tapping into alternative sources of data beyond self-reports to provide a new avenue for measuring behavioral constructs. Use of alternative data sources such as wearable sensors is necessary for developing theory and enhancing organizational practice. Although wearable sensors are now commercially available, the veracity of the data they capture is largely unknown and mostly based on manufacturers’ claims. The goal of this research is to test the validity and reliability of data captured by one such wearable badge (by Humanyze) in the context of structured meetings where all individuals wear a badge for the duration of the encounter. We developed a series of studies, each targeting a specific sensor of this badge that is relevant for structured meetings, and we make specific recommendations for badge data usage based on our validation results. We have incorporated the insights from our studies on a website that researchers can use to conduct validation tests for their badges, upload their data, and assess the validity of the data. We discuss this website in the corresponding studies.     

BIG DATA: NEW SCIENCE,NEW CHALLENGES,NEW DIALOGICAL OPPORTUNITIES

The advent of extremely large data sets, known as “big data,” has been heralded as the instantiation of a new science, requiring a new kind of practitioner: the “data scientist.” This article explores the concept of big data, drawing attention to a number of new issues—not least ethical concerns, and questions surrounding interpretation—which big data sets present. It is observed that the skills required for data scientists are in some respects closer to those traditionally associated with the arts and humanities than to those associated with the natural sciences; and it is urged that big data presents new opportunities for dialogue, especially concerning hermeneutical issues, for theologians and data scientists.     

Ethical Issues and Guidelines for Conducting Data Analysis in Psychological Research

Psychologists are directed by ethical guidelines in most areas of their practice. However, there are very few guidelines for conducting data analysis in research. The aim of this article is to address the need for more extensive ethical guidelines for researchers who are post–data collection and beginning their data analyses. Improper data analysis is an ethical issue because it can result in publishing false or misleading conclusions. This article includes a review of ethical implications of improper data analysis and potential causes of unethical practices. In addition, current guidelines in psychology and other areas (e.g., American Psychological Association and American Statistical Association Ethics Codes) were used to inspire a list of recommendations for ethical conduct in data analysis that is appropriate for researchers in psychology.     

Analysis of clustered data in community psychology: With an example from a worksite smoking cessation project

Although it is common in community psychology research to have data at both the community, or cluster, and individual level, the analysis of such clustered data often presents difficulties for many researchers. Since the individuals within the cluster cannot be assumed to be independent, the use of many traditional statistical techniques that assumes independence of observations is problematic. Further, there is often interest in assessing the degree of dependence in the data resulting from the clustering of individuals within communities. In this paper, a random-effects regression model is described for analysis of clustered data. Unlike ordinary regression analysis of clustered data, random-effects regression models do not assume that each observation is independent, but do assume data within clusters are dependent to some degree. The degree of this dependency is estimated along with estimates of the usual model parameters, thus adjusting these effects for the dependency resulting from the clustering of the data. Models are described for both continuous and dichotomous outcome variables, and available statistical software for these models is discussed. An analysis of a data set where individuals are clustered within firms is used to illustrate fetatures of random-effects regression analysis, relative to both individual-level analysis which ignores the clustering of the data, and cluster-level analysis which aggregates the individual data. Preparation of this article was supported by National Heart, Lung, and Blood Institute Grant R18 HL42987-01A1, National Institutes of Mental Health Grant MH44826-01A2, and University of Illinois at Chicago Prevention Research Center Developmental Project CDC Grant R48/CCR505025.     

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

Gait data are typically collected in multivariate form, so some multivariate analysis is often used to understand interrelationships between observed data. Principal Component Analysis (PCA), a data reduction technique for correlated multivariate data, has been widely applied by gait analysts to investigate patterns of association in gait waveform data (e.g., interrelationships between joint angle waveforms from different subjects and/or joints). Despite its widespread use in gait analysis, PCA is for two-mode data, whereas gait data are often collected in higher-mode form. In this paper, we present the benefits of analyzing gait data via Parallel Factor Analysis (Parafac), which is a component analysis model designed for three- or higher-mode data. Using three-mode joint angle waveform data (subjects×time×joints), we demonstrate Parafac's ability to (a) determine interpretable components revealing the primary interrelationships between lower-limb joints in healthy gait and (b) identify interpretable components revealing the fundamental differences between normal and perturbed subjects' gait patterns across multiple joints. Our results offer evidence of the complex interconnections that exist between lower-limb joints and limb segments in both normal and abnormal gaits, confirming the need for the simultaneous analysis of multi-joint gait waveform data (especially when studying perturbed gait patterns).     

校正的Bootstrap方法对概化理论方差分量及其变异量估计的改善

Bootstrap方法是一种有放回的再抽样方法, 可用于概化理论的方差分量及其变异量估计。用Monte Carlo技术模拟四种分布数据, 分别是正态分布、二项分布、多项分布和偏态分布数据。基于p×i设计, 探讨校正的Bootstrap方法相对于未校正的Bootstrap方法, 是否改善了概化理论估计四种模拟分布数据的方差分量及其变异量。结果表明：跨越四种分布数据, 从整体到局部, 不论是“点估计”还是“变异量”估计, 校正的Bootstrap方法都要优于未校正的Bootstrap方法, 校正的Bootstrap方法改善了概化理论方差分量及其变异量估计。     

An Excel macro for transformed and weighted averaging

An Excel macro is presented for averaging spreadsheet data. The macro has several special features: (1) The data are weighted by the inverse variance of each datum to decrease the contribution-of noisy outliers. (2) There is a provision for a power or a log transform of the data before averaging. The rationale for transforming the data before averaging is discussed (3) The output includes the average value, its standard error, and the reduced chi-square that measures the goodness of fit (4) The standard error is corrected by a heterogeneity factor based on the reduced chi-square The averaging of data is rarely done properly, and the intent of this article is to clarify the issues and provide a tool that allows researchers to improve their averaging techniques.     

Clusterwise simultaneous component analysis for analyzing structural differences in multivariate multiblock data

Many studies yield multivariate multiblock data, that is, multiple data blocks that all involve the same set of variables (e.g., the scores of different groups of subjects on the same set of variables). The question then rises whether the same processes underlie the different data blocks. To explore the structure of such multivariate multiblock data, component analysis can be very useful. Specifically, 2 approaches are often applied: principal component analysis (PCA) on each data block separately and different variants of simultaneous component analysis (SCA) on all data blocks simultaneously. The PCA approach yields a different loading matrix for each data block and is thus not useful for discovering structural similarities. The SCA approach may fail to yield insight into structural differences, since the obtained loading matrix is identical for all data blocks. We introduce a new generic modeling strategy, called clusterwise SCA, that comprises the separate PCA approach and SCA as special cases. The key idea behind clusterwise SCA is that the data blocks form a few clusters, where data blocks that belong to the same cluster are modeled with SCA and thus have the same structure, and different clusters have different underlying structures. In this article, we use the SCA variant that imposes equal average cross-products constraints (ECP). An algorithm for fitting clusterwise SCA-ECP solutions is proposed and evaluated in a simulation study. Finally, the usefulness of clusterwise SCA is illustrated by empirical examples from eating disorder research and social psychology.     

Psysquash: a program for the analysis of PsyScope data files

Many experiment-running programs generate output files that require selection, reduction, and formatting of the raw data before the numbers are suitable for input into statistical packages. PsySquash is a Macintosh program for the selection, organization, and summary of the tabular data that are produced by a widely used freeware data acquisition system, PsyScope. PsySquash serves as a bridge between PsyScope’s output data format and the input formats required by common statistical packages such as SAS, SPSS, and SuperAnova. An extension of PsySquash is proposed for use with arbitrary tabular data.     

A rotating buffer system for on-line collection of eye monitor data

Eye movement research often requires the rapid collection and handling of large amounts of data. Such collection would be impossible without the laboratory computer. This paper describes an eye movement data collection system developed for the DEC PDP-11/03 computer. The two central features of the system are a rotating buffer, which saves eye movement data in memory, and direct memory access routines for writing the data to disk. The paper also describes a procedure for testing this or any data collection system, provided the data sampling rate is known. The system presented here is sufficiently general that with slight modifications, it could be used for collecting a wide range of physiological responses, including evoked potentials.     

Comparing observational software with paper and pencil for time-sampled data: A field test of Interval Manager (INTMAN)

In this article, we describe the Interval Manager (INTMAN) software system for collecting timesampled observational data and present a preliminary application comparing the program with a traditional paper-and-pencil method. INTMAN is a computer-assisted alternative to traditional paper-and-pencil methods for collecting fixed interval time-sampled observational data. The INTMAN data collection software runs on Pocket PC handheld computers and includes a desktop application for Microsoft Windows that is used for data analysis. Standard analysis options include modified frequencies, percent of intervals, conditional probabilities, and kappa agreement matrices and values. INTMAN and a standardized paper-and-pencil method were compared under identical conditions on five dimensions: setup time, duration of data entry, duration of interobserver agreement calculations, accuracy, and cost. Overall, the computer-assisted program was a more efficient and accurate data collection system for time-sampled data than the traditional method.     

Evaluating and reporting data quality in eye movement research

In order to judge the degree of confidence one should have in the results of an experiment using eye movement records as data, it is necessary to have information about the quality of the eye movement data themselves. Suggestions are made for ways of assessing and reporting this information. The paper deals with three areas: characteristics of the eye movement signal, algorithms used in reducing the data, and accuracy of the eye position data. It is suggested that all studies involving eye movement data should report such information. Appendices include linear interpolation algorithms for mapping from the eye movement signal to stimulus space and a way of obtaining an index of accuracy for each data point.     

Postmodeling Sensitivity Analysis to Detect the Effect of Missing Data Mechanisms

Incomplete or missing data is a common problem in almost all areas of empirical research. It is well known that simple and ad hoc methods such as complete case analysis or mean imputation can lead to biased and/or inefficient estimates. The method of maximum likelihood works well; however, when the missing data mechanism is not one of missing completely at random (MCAR) or missing at random (MAR), it too can result in incorrect inference. Statistical tests for MCAR have been proposed, but these are restricted to a certain class of problems. The idea of sensitivity analysis as a means to detect the missing data mechanism has been proposed in the statistics literature in conjunction with selection models where conjointly the data and missing data mechanism are modeled. Our approach is different here in that we do not model the missing data mechanism but use the data at hand to examine the sensitivity of a given model to the missing data mechanism. Our methodology is meant to raise a flag for researchers when the assumptions of MCAR (or MAR) do not hold. To our knowledge, no specific proposal for sensitivity analysis has been set forth in the area of structural equation models (SEM). This article gives a specific method for performing postmodeling sensitivity analysis using a statistical test and graphs. A simulation study is performed to assess the methodology in the context of structural equation models. This study shows success of the method, especially when the sample size is 300 or more and the percentage of missing data is 20% or more. The method is also used to study a set of real data measuring physical and social self-concepts in 463 Nigerian adolescents using a factor analysis model.     

Judgments about the duration of brief stimuli

Visual duration discrimination data for durations between 70 and 1,020 msec are presented. A model for duration discrimination proposed by Allan, Kristofferson, and Wiens (1972) is elaborated, and the data are discussed in terms of the model. The data axe in agreement with the basic assumptions of the model. Differences between our data and duration discrimination data presented by others are discussed.     

How to Generate Non-normal Data for Simulation of Structural Equation Models

A procedure for generating non-normal data for simulation of structural equation models is proposed. A simple transformation of univariate random variables is used for the generation of data on latent and error variables under some restrictions for the elements of the covariance matrices for these variables. Data on the observed variables is then computed from latent and error variables according to the model. It is shown that by controlling univariate skewness and kurtosis on pre-specified random latent and error variables, observed variables can be made to have a relatively wide range of univariate skewness and kurtosis characteristics according to the pre-specified model. Univariate distributions are used for the generation of data which enables a user to choose from a large number of different distributions. The use of the proposed procedure is illustrated for two different structural equation models and it is shown how PRELIS can be used to generate the data.