Carl J Huberty
Biographic Data
| ID | 1661706 |
|---|---|
| NAME | Carl J Huberty |
| GIVEN NAMES | Carl J |
| FAMILY NAME | Huberty |
| SIGNATURE | HUBERTY C J |
| AFFILIATIONS | University of Georgia |
| VERIFIED | No |
| TOTAL WORKS | 31 |
| TOTAL CITATIONS | 93 |
| AUTHOR COUNT | 31 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1971 |
| LATEST PUBLICATION YEAR | 2009 |
| H-INDEX | 5 |
Talk During Book Sharing Between Parents and Preschool Children
The purpose of the current study was to explore parent and child extratextual utterances during storybook and expository book sharing. Sixty‐two parents and their three‐ to four‐year‐old children from middle‐income families participated in the study. Dyads were videotaped on two occasions reading unfamiliar storybooks and expository books. Parent and child extratextual utterances were coded for their content, and parent utterances were coded for …
Multiple Correlation Versus Multiple Regression
It is well known that multiple correlation analysis (MCA) calculations and multiple regression analysis (MRA) calculations overlap a bit. What has not been routinely recognized is that the two analysis procedures involve different research questions and study designs, different inferential approaches, different analysis strategies, and different reported information. These differences are described
Patterns of parents' extratextual interactions during book sharing with preschool children
S Ninety‐six middle‐income parent‐child dyads were videotaped as they shared an unfamiliar book together. Parents' extratextual utterances were coded for content and entered into a cluster analysis in order to identify patterns in the variability in interaction style. Four clusters were revealed: (a) two small clusters of parents who provided many extratextual utterances during book sharing, but who differed with regard to the types of utterances…
A History of Effect Size Indices
Depending on how one interprets what an effect size index is, it may be claimed that its history started around 1940, or about 100 years prior to that. An attempt is made in this article to trace histories of a variety of effect size indices. Effect size bases discussed pertain to (a) relationship, (b) group differences, and (c) group overlap. Multivariable as well as univariate indices are considered in reviewing the histories
The Efficacy of Two Improvement-Over-Chance Effect Sizes for Two-Group Univariate Comparisons Under Variance Heterogeneity and Nonnormality
The efficacy of two improvement-over-chance or I effect sizes, derived from predictive discriminant analysis (PDA) and logistic regression analysis (LRA), was investigated for two-group univariate mean comparisons. Data were generated under selected levels of population separation, variance pattern, sample size, and distribution shape. Based on the accuracy of sample estimates, both I indices are acceptable under optimal conditions except when bo…
Group Overlap as a Basis for Effect Size
The research content of interest herein is that of comparison of means. It is generally recognized that statistical test p values do not adequately reflect mean comparison assessments. What is desirable is some effect-size assessment. The typical effect-size indexes used in mean comparisons are restricted to the variance homogeneity condition. What is proposed here is the use of the group-overlap concept. Group overlap may be assessed via predict…
Statistical Practices of Educational Researchers
Articles published in several prominent educational journals were examined to investigate the use of data analysis tools by researchers in four research paradigms: between-subjects univariate designs, between-subjects multivariate designs, repeated measures designs, and covariance designs. In addition to examining specific details pertaining to the research design (e.g., sample size, group size equality/inequality) and methods employed for data a…
Evaluation of a state critical thinking skills training program
Discriminant Analysis Via Statistical Packages
The two aspects of discriminant analysis are briefly reviewed: predictive discriminant analysis (PDA) and descriptive discriminant analysis (DDA). Use of three popular statistical packages (BMDP, SAS, and SPSS) to obtain computational results for a PDA and for a DDA is generally reviewed. Results yielded by two BMDP procedures (7M and SM) are discussed, as are four SAS procedures (DISCRIM, STEPDISC, CANDISC, and GLM), and two SPSS procedures (DIS…
Behavioral Clustering of School Children
The intent of this article is to illustrate how a cluster analysis might be conducted, validated, and interpreted. Data normed for a behavioral assessment instrument with 14 scales on a sample drawn from a nationally representative pool of U.S. school children were utilized. The analysis discussed covers the cluster method, cluster typology, cluster validity, cluster structure, and prediction of cluster membership
Why Multivariable Analyses
Following a brief interpretation of a multivariate analysis, purposes of such an analysis are reviewed. A discussion of two primary purposes, prediction and structure identification, and of the secondary purpose of response variable ordering is given. Concluding comments are made pertaining to the necessity of a sound initial choice of response variables and to the questionable use of a multivariate analysis preliminary to simpler analyses if the…
Relations among Dimensions of Statistical Knowledge
Ways of knowing statistical concepts are reviewed. A general three-category structure for knowing is proposed: (a) calculations, (b) propositions, and (c) conceptual understandings. Test items were developed that correspond to the first category and to a partitioning of the two latter categories into words and symbols. Thirty-one items covering the five types were administered to 57 graduate students. Correlation of student scores on the 10-item …
An Introduction to Discriminant Analysis
An intent of this article is to provide an interpretation of “discriminant analysis” that includes two sets of techniques, predictive discriminant analysis and descriptive discriminant analysis. The basic purposes of these two analyses are discussed. Common introductory practices associated with current applications of predictive and descriptive discriminant analysis are reviewed, as are additional practices that might be applied and questionable…
Multivariate analysis versus multiple univariate analyses
The argument for preceding multiple analysis of variance (ANOVAS) with a multivariate analysis of variance (MANOVA) to control for Type I error is challenged. Several situations are discussed in which multiple ANOVAS might be conducted without the necessity of a preliminary MANOVA. Three reasons for considering a multivariate analysis are discussed: to identify outcome variable system constructs, to select variable subsets, and to determine varia…
A Single Contrast Test Procedure
The thesis of this article is that the multitude of procedures for testing hypotheses about mean contrasts often presented in statistical methods textbooks is seemingly unwarranted. Nearly all research situations calling for the study of contrasts can be handled with a single contrast test statistic, that often attributed to R. A. Fisher. By jointly considering a probability value and an eta-squared value and by keeping in mind the total number o…
Clarification
Clarification
Assessing Predictive Accuracy in Discriminant Analysis
The estimation of probabilities of correct classification is a primary concern in predictive discriminant analysis. Three such probabilities are: (a) the optimal hit rate, that obtained when the classification rule is based on known parameters; (b) the actual hit rate, that obtained by applying a rule based on a particular sample to future samples; and (c) the expected actual hit rate. Methods of estimating these hit rates include formulas (in th…
Selecting a Two-Group Classification Weighting Algorithm
The cross-validated classification accuracies of three predictor weighting strategies (least squares, ridge regression, and reduced rank) were compared under varying simulated data conditions for the two-group classification problem. Results were somewhat similar to previous findings with multiple regression when absolute rather than relative cross-validation accuracy was the criterion. The alternate weighting strategies performed better than the…
On Statistical Testing
The statistical testing approaches of Hypothesis Testing (Fisher) and Significance Testing (Neyman-Pearson) are briefly reviewed. Highly related notions of alpha-values, P-values, and magnitude-of-effect are discussed. A statistical testing approach that is a hybrid of Hypothesis Testing and Significance Testing is then advanced. This approach takes into consideration the two decision-error probabilities as well as an alternative hypothesis chara…
Using Categorical Variables in Discriminant Analysis
Three methods of transforming unordered categorical response variables are described. One is a method using dummy variables. The second method, in which all categorical variables are analyzed simultaneously, is based on an eigenanalysis of frequency patterns scaled relative to within-groups variance, jointly developed by J. E. Overall and J. A. Woodward. With the third method, independently developed by R. A. Fisher and H. O. Lancaster, each cate…
Issues in the use and interpretation of discriminant analysis
Two-Group Comparisons and Univariate Classification
An alternative analysis of the traditional two-group single response variable design is discussed. The proposed analysis involves the classification, or assignment, of experimental units to populations represented by the two groups. Methods of estimating probabilities of correct classification are discussed. Three real data sets are provided to illustrate the utility of a classification analysis in describing both group as well as individual diff…
Some Univariate-Multivariate Generalizations
The mathematical extension from scalars to matrices as a prerequisite for univariate to multivariate generalizations of statistical concepts and methods is discussed. The basic notion of variability is generalized from a univariate context to a multivariate context using two matrix functions, a determinant and a trace. These two means of generalizing variance are shown to yield a number of alternative multivariate indices of shared variation. Mul…
The Study Of Effects In Manova
A particular strategy for investigating effects resulting from a MANOVA is proposed. The strategy involves multiple two-group multivariate analyses. The two groups result from considering multivariate pairwise group contrasts or multivariate complex group contrasts. Assuming a given two-group analysis yields real effects, the resultant single linear discriminant function (LDF) may be studied. A rationale based on a transformation of LDF weights, …
Multivariate analysis versus multiple univariate analyses
The argument for preceding multiple analysis of variance (ANOVAS) with a multivariate analysis of variance (MANOVA) to control for Type I error is challenged. Several situations are discussed in which multiple ANOVAS might be conducted without the necessity of a preliminary MANOVA. Three reasons for considering a multivariate analysis are discussed: to identify outcome variable system constructs, to select variable subsets, and to determine varia…
Issues in the use and interpretation of discriminant analysis
Patterns of parents' extratextual interactions during book sharing with preschool children
S Ninety‐six middle‐income parent‐child dyads were videotaped as they shared an unfamiliar book together. Parents' extratextual utterances were coded for content and entered into a cluster analysis in order to identify patterns in the variability in interaction style. Four clusters were revealed: (a) two small clusters of parents who provided many extratextual utterances during book sharing, but who differed with regard to the types of utterances…
Talk During Book Sharing Between Parents and Preschool Children
The purpose of the current study was to explore parent and child extratextual utterances during storybook and expository book sharing. Sixty‐two parents and their three‐ to four‐year‐old children from middle‐income families participated in the study. Dyads were videotaped on two occasions reading unfamiliar storybooks and expository books. Parent and child extratextual utterances were coded for their content, and parent utterances were coded for …
An Introduction to Discriminant Analysis
An intent of this article is to provide an interpretation of “discriminant analysis” that includes two sets of techniques, predictive discriminant analysis and descriptive discriminant analysis. The basic purposes of these two analyses are discussed. Common introductory practices associated with current applications of predictive and descriptive discriminant analysis are reviewed, as are additional practices that might be applied and questionable…
On Statistical Testing
The statistical testing approaches of Hypothesis Testing (Fisher) and Significance Testing (Neyman-Pearson) are briefly reviewed. Highly related notions of alpha-values, P-values, and magnitude-of-effect are discussed. A statistical testing approach that is a hybrid of Hypothesis Testing and Significance Testing is then advanced. This approach takes into consideration the two decision-error probabilities as well as an alternative hypothesis chara…
Brief Report
The equivalence of two multivariate classification schemes is shown when the sizes of the samples drawn from the populations to which assignment is required are identical. One scheme is based on posterior probabilities determined from a Bayesian density function; the second scheme is based on likelihood ratio discriminated scores. Both of these procedures involve prior probabilities; if estimates of these priors are obtained from the identical sa…
Multivariate Indices of Strength of Association
Multivariate extensions of univariate measures of association that are employed in comparative experiments are reviewed. Two of these are generalizations of the correlation ratio, and the third is a generalization of Hays' omega. The numerical values of these three indices are empirically compared along with a fourth index, the proportion of correct classifications, using two sets of data and various numbers of predictor variables. As in the univ…
An Empirical Comparison of the Accuracy of Selected Multivariate Classification Rules
This study involved two phases: first, when classification was based on the calibration sample; and second, in a cross-validation setting. Computer generated data were used. Results obtained from rules based on probabilities of group membership were compared for accuracy when classifying in the discriminant space and in the predictor variable spaces. In the first phase accuracy was greater in the predictor variable spaces, while the reverse was t…
Use of Some "Discriminant Analysis" Computer Programs
The objective of this paper is to review the outputs of selected computer programs often used to carry out a "discriminant analysis" with respect to two purposes of such an analysis: (1) discrimination, and (2) classification. The programs selected are three BMD programs. Information provided by the programs in terms of requisite data conditions for each type of analysis is discussed. The information yielded directly by any of the programs is qui…
Linear Versus Quadratic Multivariate Classification
A linear classification rule (used with equal covariance matrices) was contrasted with a quadratic rule (used with unequal covariance matrices) for accuracy of internal and external classification. The comparisons were made for seven situations which resulted from combining conditions (equal and unequal covariance matrices, and two and three criterion groups) for different sets of real data. For the internal analysis the quadratic rule was superi…
Estimation in Multiple Correlation/Prediction
The distinction between the square of a population correlation coefficient (ρ 2 ) and of the true validity of a sample prediction equation (ρ v 2 ) as the parameters of interest in multiple correlation and in multiple prediction studies, respectively, is discussed. Real data are used to illustrate the comparison of two estimators for ρ 2 , R R 2 (Ezekiel) and R OP 2 (Olkin and Pratt), and of three estimators for ρ v 2 , R NL 2 (Nicholson, Lord), …
The Study Of Effects In Manova
A particular strategy for investigating effects resulting from a MANOVA is proposed. The strategy involves multiple two-group multivariate analyses. The two groups result from considering multivariate pairwise group contrasts or multivariate complex group contrasts. Assuming a given two-group analysis yields real effects, the resultant single linear discriminant function (LDF) may be studied. A rationale based on a transformation of LDF weights, …
Two-Group Comparisons and Univariate Classification
An alternative analysis of the traditional two-group single response variable design is discussed. The proposed analysis involves the classification, or assignment, of experimental units to populations represented by the two groups. Methods of estimating probabilities of correct classification are discussed. Three real data sets are provided to illustrate the utility of a classification analysis in describing both group as well as individual diff…
Some Univariate-Multivariate Generalizations
The mathematical extension from scalars to matrices as a prerequisite for univariate to multivariate generalizations of statistical concepts and methods is discussed. The basic notion of variability is generalized from a univariate context to a multivariate context using two matrix functions, a determinant and a trace. These two means of generalizing variance are shown to yield a number of alternative multivariate indices of shared variation. Mul…
Issues in the use and interpretation of discriminant analysis
Using Categorical Variables in Discriminant Analysis
Three methods of transforming unordered categorical response variables are described. One is a method using dummy variables. The second method, in which all categorical variables are analyzed simultaneously, is based on an eigenanalysis of frequency patterns scaled relative to within-groups variance, jointly developed by J. E. Overall and J. A. Woodward. With the third method, independently developed by R. A. Fisher and H. O. Lancaster, each cate…
Assessing Predictive Accuracy in Discriminant Analysis
The estimation of probabilities of correct classification is a primary concern in predictive discriminant analysis. Three such probabilities are: (a) the optimal hit rate, that obtained when the classification rule is based on known parameters; (b) the actual hit rate, that obtained by applying a rule based on a particular sample to future samples; and (c) the expected actual hit rate. Methods of estimating these hit rates include formulas (in th…
Selecting a Two-Group Classification Weighting Algorithm
The cross-validated classification accuracies of three predictor weighting strategies (least squares, ridge regression, and reduced rank) were compared under varying simulated data conditions for the two-group classification problem. Results were somewhat similar to previous findings with multiple regression when absolute rather than relative cross-validation accuracy was the criterion. The alternate weighting strategies performed better than the…
On Statistical Testing
The statistical testing approaches of Hypothesis Testing (Fisher) and Significance Testing (Neyman-Pearson) are briefly reviewed. Highly related notions of alpha-values, P-values, and magnitude-of-effect are discussed. A statistical testing approach that is a hybrid of Hypothesis Testing and Significance Testing is then advanced. This approach takes into consideration the two decision-error probabilities as well as an alternative hypothesis chara…
A Single Contrast Test Procedure
The thesis of this article is that the multitude of procedures for testing hypotheses about mean contrasts often presented in statistical methods textbooks is seemingly unwarranted. Nearly all research situations calling for the study of contrasts can be handled with a single contrast test statistic, that often attributed to R. A. Fisher. By jointly considering a probability value and an eta-squared value and by keeping in mind the total number o…
Clarification
Clarification
An Introduction to Discriminant Analysis
An intent of this article is to provide an interpretation of “discriminant analysis” that includes two sets of techniques, predictive discriminant analysis and descriptive discriminant analysis. The basic purposes of these two analyses are discussed. Common introductory practices associated with current applications of predictive and descriptive discriminant analysis are reviewed, as are additional practices that might be applied and questionable…
Multivariate analysis versus multiple univariate analyses
The argument for preceding multiple analysis of variance (ANOVAS) with a multivariate analysis of variance (MANOVA) to control for Type I error is challenged. Several situations are discussed in which multiple ANOVAS might be conducted without the necessity of a preliminary MANOVA. Three reasons for considering a multivariate analysis are discussed: to identify outcome variable system constructs, to select variable subsets, and to determine varia…
Relations among Dimensions of Statistical Knowledge
Ways of knowing statistical concepts are reviewed. A general three-category structure for knowing is proposed: (a) calculations, (b) propositions, and (c) conceptual understandings. Test items were developed that correspond to the first category and to a partitioning of the two latter categories into words and symbols. Thirty-one items covering the five types were administered to 57 graduate students. Correlation of student scores on the 10-item …
Why Multivariable Analyses
Following a brief interpretation of a multivariate analysis, purposes of such an analysis are reviewed. A discussion of two primary purposes, prediction and structure identification, and of the secondary purpose of response variable ordering is given. Concluding comments are made pertaining to the necessity of a sound initial choice of response variables and to the questionable use of a multivariate analysis preliminary to simpler analyses if the…
Discriminant Analysis Via Statistical Packages
The two aspects of discriminant analysis are briefly reviewed: predictive discriminant analysis (PDA) and descriptive discriminant analysis (DDA). Use of three popular statistical packages (BMDP, SAS, and SPSS) to obtain computational results for a PDA and for a DDA is generally reviewed. Results yielded by two BMDP procedures (7M and SM) are discussed, as are four SAS procedures (DISCRIM, STEPDISC, CANDISC, and GLM), and two SPSS procedures (DIS…
Behavioral Clustering of School Children
The intent of this article is to illustrate how a cluster analysis might be conducted, validated, and interpreted. Data normed for a behavioral assessment instrument with 14 scales on a sample drawn from a nationally representative pool of U.S. school children were utilized. The analysis discussed covers the cluster method, cluster typology, cluster validity, cluster structure, and prediction of cluster membership
Statistical Practices of Educational Researchers
Articles published in several prominent educational journals were examined to investigate the use of data analysis tools by researchers in four research paradigms: between-subjects univariate designs, between-subjects multivariate designs, repeated measures designs, and covariance designs. In addition to examining specific details pertaining to the research design (e.g., sample size, group size equality/inequality) and methods employed for data a…
Evaluation of a state critical thinking skills training program
Mathematics (27 works) · Statistics (27 works) · Advanced Statistical Methods and Models (22 works) · Computer Science (21 works) · Psychology (17 works) · Econometrics (13 works) · Multivariate statistics (13 works) · Artificial Intelligence (10 works) · Artificial Intelligence (10 works) · Linear discriminant analysis (10 works)