Advanced Quantitative Data AnalysisOpen University Press, 2003 - 254 pagine What do advanced statistical techniques do? When is it appropriate to use them? How are they carried out and reported? There are a variety of statistical techniques used to analyse quantitative data that masters students, advanced undergraduates and researchers in the social sciences are expected to be able to understand and undertake. This book explains these techniques, when it is appropriate to use them, how to carry them out and how to write up the results. Most books which describe these techniques do so at too advanced or technical a level to be readily understood by many students who need to use them. In contrast, the following features characterise this book: concise and accessible introduction to calculating and interpreting advanced statistical techniques; use of a small data set of simple numbers specifically designed to illustrate the nature and manual calculation of the most important statistics in each technique; succinct illustration of writing up the results of these analyses; minimum of mathematical, statistical and technical notation; and, annotated bibliography and glossary of key concepts.; Commonly used software is introduced, and instructions are presented for carrying out analyses and interpreting the output using the computer programs of SPSS Release 11 for Windows and a version of LISREL 8.51, which is freely available online. Designed as a textbook for postgraduate and advanced undergraduate courses across the socio-behavioural sciences, this book will also serve as a personal reference for researchers in disciplines such as sociology and psychology. |
Sommario
Introduction | 1 |
Exploratory factor analysis | 25 |
Confirmatory factor analysis | 39 |
Copyright | |
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analysis of covariance analysis of variance between-groups box in Box button to put calculated Chapter child's ability child's academic achievement child's interest cluster analysis column criterion Data Editor degrees of freedom dependent variable dialog box differ significantly discriminant analysis discriminant function dummy variables error example expected frequencies F ratio factor analysis interest and academic Levene's test likelihood ratio chi-square LISREL output log likelihood log-linear analysis logistic regression marital status mean square method multiple regression never married number of groups number of predictors one-way analysis parameters parents partial correlation partial regression coefficient path analysis path coefficients path diagram predicted probability predictor variables principal components proportion of variance qualitative quantitative variables regression analysis relationship sample score statistic shown in Table SPSS output squared multiple correlation standardized partial regression statistically significant step sub-dialog box sum of squares teachers techniques third model three groups two-tailed Univariate unstandardized variance in academic within-groups