Chapter 11 Personality

A regression grouping technique is described for reducing the number of predictor variables while retaining predictive information. The data used for illustration are principal responses to the Rokeach dogmatism items as predictors, and school bureaucratization as the criterion. The dogmatism-bureaucratization relationship is analyzed by means of this grouping technique. The results are contrasted with those using the more usual techniques of summing and factor analysis, and are shown to lead to very different conclusions. The merits, assumptions, and potential generalizability of this grouping technique are discussed.     

A comparison of two methods for analyzing longitudinal data: Tuckerized growth curves and an application of K means analysis

Two longitudinal statistical methods are illustrated and compared. The first method, Tuckerized growth curve analysis (TGCA), is an extension of factor analysis, while the second method is an extension of K means analysis (KMA), a clustering technique. Both techniques make few assumptions of data and are useful for examining growth or change in longitudinal data. The two statistical methods are described and illustrated with group counseling data. The relative strengths and weaknesses of each technique are discussed.     

USING ANALYSIS OF VARIANCE WITH A DICHOTOMOUS DEPENDENT VARIABLE: AN EMPIRICAL STUDY1

A study was conducted to determine if analysis of variance techniques are appropriate when the dependent variable has a dichotomous (zero-one) distribution. Several 1-, 2-, and 3-way analysis of variance configurations were investigated with regard to both the size of the Type I error and the Power. The findings show the analysis of variance to be an appropriate statistical technique for analyzing dichotomous data in fixed effects models where cell frequencies are equal under the following conditions: (a) the proportion of responses in the smaller response category is equal to or greater than .2 and there are at least 20 degrees of freedom for error, or (b) the proportion of responses in the smaller response category is less than .2 and there are at least 40 degrees of freedom for error.     

More on time series designs: A reanalysis of mayer and Kozlow's data

A recent article by Mayer and Kozlow introduces a time series experiment to science education researchers. This article examines in greater detail design considerations and reanalyzes their data using time series analysis. The first applications of time series experimental methodology to science education research were reported by Mayer and Lewis (1979) and by Mayer and Kozlow (1980). This article discusses Mayer and Kozlow's work and presents a reanalysis of their data which is intended to be heuristic, not critical. (All data were supplied by Dr. Mayer and permission was granted to use the data.) Both design and analysis will be examined. It is important to distinguish time series experimental design from time series analysis. The former, as presented by Campbell and Stanley (1966) and detailed by Glass, Willson, and Gottman (1975), is concerned with structuring treatment conditions and observations over time to make causal inferences. Time series analysis is a set of statistical techniques from which an appropriate technique can be selected to analyze collected data in the framework of a time series design. Just as data from in a randomized experiment might be analyzed using a one-way analysis of variance or a Kruskal- Wallis H-test, data from a time series design may be subjected to several alternative analysis techniques.     

Selection blas and the analysis of persistence of part-time undergraduate students

Most studies of persistence behavior use path analysis or ordinary least squares regression to estimate unknown coefficients. However, estimates produced by these techniques are biased if selectivity bias contaminates choices made by individuals in the data sample. We explain this problem, argue that it is present in data samples used in persistence studies, and discuss an alternative estimation technique that controls for it. The methodology and the differences in the interpretation of coefficient estimates are illustrated with a data sample of individual students at a single university.     

A SECOND LOOK AT ANALYSIS OF VARIANCE ON DICHOTOMOUS DATA

The work of Lunney (1970) concerning the appropriateness of analysis of variance (ANOVA) techniques on dichotomous data is discussed and extended. Relations between standard statistical techniques for analyzing dichotomous data and ANOVA procedures are indicated. The need for usefulness of analyzing transformed data as opposed to direct analysis of dichotomous data are discussed. Required statistical procedures employing transformed data are outlined.     

A Regression Approach to Experimental Design

This paper describes a regression approach to experimental design. Typically, the educational researcher is familiar with the usual analysis of variance techniques and is unaware of the versatility of multiple regression for problem solving. The specific approach used in this paper assumes only a familiarity with experimental design and access to a general purpose multiple regression program which is likely to be on hand at any computer installation. Examples of the t-test, 1-way analysis of variance, and the treatment X subjects design from a regression standpoint are given.     

Applying robust regression techniques to institutional data

Regression techniques are used frequently to analyze the relationships between university activity variables and the needs for different categories of resources. The ordinary least squares method (LS) has the disadvantage of being very sensitive to outliers. As an alternative the least median of squares (LMS) technique is discussed, which can resist a large fraction of contaminated data. To demonstrate the advantages of LMS, the parameters of some regression equations, estimated some years ago by means of ordinary least squares, and describing the needs for nonacademic staff and operating funds in a university, will be reestimated by means of this robust regression technique.     

A new algorithm to create balanced teams promoting more diversity

The problem of assigning students to teams can be described as maximising their profiles diversity within teams while minimising the differences among teams. This problem is commonly known as the maximally diverse grouping problem and it is usually formulated as maximising the sum of the pairwise distances among students within teams. We propose an alternative algorithm in which the within group heterogeneity is measured by the attributes' variance instead of by the sum of distances between group members. The proposed algorithm is evaluated by means of two real data sets and the results suggest that it induces better solutions according to two independent evaluation criteria, the Davies–Bouldin index and the number of dominated teams. In conclusion, the results show that it is more adequate to use the attributes' variance to measure the heterogeneity of profiles within the teams and the homogeneity among teams.     

温度变化影响下基于神经网络的悬索桥损伤预警方法

提出了考虑温度变化影响的悬索桥结构损伤预警方法.首先,采用神经网络技术建立桥梁实测模态频率与温度的相关性模型,用以消除温度变化对模态频率的影响.然后,将不同温度下的实测模态频率进行＂温度归一化＂,在此基础上利用神经网络新奇检测技术建立自联想神经网络进一步识别模态频率的异常变化.通过润扬大桥悬索桥236d的实测数据分析验证了该方法的可行性.分析结果表明,不同季节下模态频率的相对变化平均约为2.0%,采用所提方法可以识别出悬索桥模态频率0.1%的异常变化,适用于悬索桥结构的在线整体状态监测.     

面波法与单孔检层法波速测试的工程应用

简述了面波法与单孔法等波速测试方法的工作原理、现场施测技术以及数据处理和资料分析过程.以工程实例说明了波速测试技术在岩土工程勘察设计中的应用及其效果.最后就勘探工作的总体安排及其原位测试方法的选择进行了探讨.     

Middle school students' conceptual perceptions of the social studies course,and their transfer of the outcomes to daily life

The primary aim of this study was to identify middle school students' conceptual perceptions of the social studies course, and their usage and awareness of the course outcomes in daily life. Qualitative research methods and techniques were employed in the study. The participants were 34 middle school seventh-grade students from a state middle school in Istanbul, Turkey. Letter writing was used as the data-gathering tool, and the data were analyzed by means of the content analysis technique. The findings showed that the participants perceived social studies as a course consisting of social-humanistic sciences with an emphasis on history and communication in accordance with the theoretical structure of the course, and employed their outcomes from this course that they can partly use in daily life.     

Missing Data and Multiple Imputation in the Context of Multivariate Analysis of Variance

Multivariate analysis of variance (MANOVA) is widely used in educational research to compare means on multiple dependent variables across groups. Researchers faced with the problem of missing data often use multiple imputation of values in place of the missing observations. This study compares the performance of 2 methods for combining p values in the context of a MANOVA, with the typical default for dealing with missing data: listwise deletion. When data are missing at random, the new methods maintained the nominal Type I error rate and had power comparable to the complete data condition. When 40% of the data were missing completely at random, the Type I error rates for the new methods were inflated, but not for lower percents.     

The Incorporation of Sample Weights Into Multilevel Structural Equation Models

Over the past decade and a half, methodologists working with structural equation modeling (SEM) have developed approaches for accommodating multilevel data. These approaches are particularly helpful when modeling data that come from complex sampling designs. However, most data sets that are associated with complex sampling designs also include observation weights, and methods to incorporate these sampling weights into multilevel SEM analyses have not been addressed. This article investigates the use of different weighting techniques and finds, through a simulation study, that the use of an effective sample size weight provides unbiased estimates of key parameters and their sampling variances. Also, a popular normalization technique of scaling weights to reflect the actual sample size is shown to produce negatively biased sampling variance estimates, as well as negatively biased within-group variance parameter estimates in the small group size case.     

Instruction Characteristics and Cognitive Achievement of Young Children in Elementary Schools

This ongoing Dutch study into the school careers of young children in elementary schools, facilitated by the Educational Priority Policy, has focused on the influence of school and class organization on linguistic and cognitive development. In this article, the development in word decoding, reading comprehension, and math of 3rd graders (6 years of age) is analyzed. The data are gathered in 28 elementary schools by means of tests, questionnaires, and logbooks. A multilevel analysis shows that for word decoding and reading comprehension differences in development caused by differences in instruction are very small. For math, on the contrary, there is a substantial proportion of variance at the class level. Instruction characteristics and years of experience by the teachers seemed to be important.     

Randomized Response Analysis in Mplus

This article describes a technique to analyze randomized response data using available structural equation modeling (SEM) software. The randomized response technique was developed to obtain estimates that are more valid when studying sensitive topics. The basic feature of all randomized response methods is that the data are deliberately contaminated with error. This makes it difficult to relate randomized responses to explanatory variables. In this tutorial, we present an approach to this problem, in which the analysis of randomized response data is viewed as a latent class problem, with different latent classes for the random and the truthful responses. To illustrate this technique, an example is presented using the program Mplus.     

Application of three-dimensional visual simulation in tidal defense engineering

Any tidal defense engineering involves the collection and analysis of massive information about engineering structures and their surrounding environment. Traditional method, which is carried out mainly by means of twodimensional drawings and textures, is not efficient and intuitive enough to analyze the whole project and reflect its spatial relationship. Three-dimensional visual simulation provides an advanced technical means of solving this problem. In this paper, triangular irregular network (TIN) model simplified by non-uniform rational B-splines (NURBS) technique was used to establish the digital terrain model (DTM) of a super large region. Simulation of dynamic water surface was realized by combining noise function with sine wave superposition method. Models of different objects were established with different modeling techniques according to their characteristics. Application of texture mapping technology remarkably improved the authenticity of the models. Taking the tidal defense engineering in the new coastal region of Tianjin as a case study, three-dimensional visual simulation and dynamic roaming of the study area were realized, providing visual analysis and visible demonstration method for the management and emergency decision-making associated with construction.     

基于数据仓库技术的电子商务系统

提出了基于数据仓库技术,把客户点击流数据和电子商务网站产品交易数据相结合,共同进行智能分析,提供个性化推荐的思想.介绍了建立基于数据仓库技术的电子商务系统的关键技术,讨论了点击流分析技术,重点介绍了采用JAVA技术设计的图书电子商务系统,阐述了其点击流的收集、分析和个性化推荐的实现.     

Repeated-measures analysis of variance in developmental research: selected issues

This paper presents a review of recent developments in statistical techniques for repeated-measures analysis of variance. Since the literature has emphasized the issue of mixed model assumptions and their violation, we present an updated perspective on the nature of these assumptions and their implications for mixed model, adjusted mixed model, or multivariate significance tests. However, the central theme of the review is that the validity of mixed model assumptions is but one consideration in selection of an appropriate method of repeated-measures ANOVA. In particular, we recommend the avoidance of omnibus significance tests in favor of specific planned comparisons whenever hypotheses more specific than the omnibus null hypothesis may be formulated a priori. The analyst must also consider whether multiple dependent measures are to be analyzed, and the paper discusses alternative approaches to true multivariate repeated-measures designs. It also includes discussion of other relevant issues, including a brief review of the strengths and weaknesses of commonly available statistical software when applied to the analysis of repeated-measures data.     

A comparative critique of several methods of collecting data for cognitive mapping

Summary Cognitive mapping by learners for various research purposes is assisted if the appropriate technique is used with learners at different stages of schooling and with varying degrees of understanding of a content field and of related content fields. There are however, techniques now available which appear to be usable by learners in primary, secondary and tertiary education. The use of the elucidatory interview is an exciting new tool for science educators. It is not, however, an easy one to use and our own experience suggests that a challenging combination of experince and naivety is needed for it to have optimal benefit as a means of rich data.