Repeated measures data arise when the same characteristic is measured on each case or subject at several times or under several conditions. There is a multitude of techniques available for analysing such data and in the past this has led to some confusion. This book describes the whole spectrum of approaches, beginning with very simple and crude methods, working through intermediate techniques commonly used by consultant statisticians, and concluding with more recent and advanced methods. Those covered include multiple testing, response feature analysis, univariate analysis of variance approaches, multivariate analysis of variance approaches, regression models, two-stage line models, approaches to categorical data and techniques for analysing crossover designs. The theory is illustrated with examples, using real data brought to the authors during their work as statistical consultants.
..."useful in a consultant statistician's work as well as for a student's statistical computer exercises and can be recommended for both cases." -Statistics
Part 1 Introduction: Background and objectives; overview. Part 2 Some simple analyses: comparisons at individual times; response feature analysis; individual curve fitting. Part 3 Univariate analysis of variance: the fundamental model; Anova; calculation of expected mean-squares; expected mean-squares by "synthesis"; contrasts, compound symmetry and F-tests; relaxing assumptions - univariate, modified univariate or multivariate tests? Part 4 Multivariate analysis: models without special covariance structure; hotellings; testing for polynomial trends; Manova. Part 5 Regression models: special case; general case; structured covariance case; some covariance structures. Part 6 Two-stage linear models: random regression coefficients; estimation and testing; particular aspects; examples. Part 7 Crossover experiments: simple 2x2 designs; a Bayesian approach to 2x2 designs; more complex crossover designs for two treatments; crossover trials with a binary response. Part 8 Categorical data: Markov chain models; log-linear models; linear model methods for group and time comparisons; randomization test approaches; some special cases. Part 9 Some further topics: some practical matters; antedependence; tracking; nonlinear growth curves; non-normal observations. Part 10 Computer software and examples: repeated measure facilities in BMDP, SPSS and SAS; example 1 - BMDP program 2V; example 2 - BMDP program 2V; example 3 - BMDP program 2V; example 4 - SPSS program MANOVA; example 5 - SPSS program MANOVA.
Series: Monographs on Statistics and Applied Probability
Number Of Pages: 272
Published: 1st May 1990
Publisher: Taylor & Francis Ltd
Country of Publication: US
Dimensions (cm): 22.23 x 14.61
Weight (kg): 0.49
Edition Number: 1