Research methods for the biosciences - Holmes, Debbie; Moody, Peter; Dine, Diana (Oxford : Oxford University Press, 2006)
Machine derived contents note: 1 Where do I begin? -- 1.1 Data 1 -- 1.2 Types of investigations 1 -- 1.3 Aims, objectives, hypotheses 5 -- 1.4 Items and observations 7 -- 1.5 Populations 8 -- 1.6 Sample 9 -- 1.7 Populations, parameters, statistics, and samples 11 -- 1.8 Treatments 12 -- 1.9 Variation and variables 13 -- 2 Experimental design -- 2.1 Evaluating published research 16 -- 2.2 Have a go! 20 -- 2.3 Questionnaires, focus groups, and interviews 36 -- 2.4 Research and the law 46 -- 2.5 Managing research 46 -- 3 What to do with raw data -- 3.1 Types of data 51 -- 3.2 Distributions of data 54 -- 3.3 Summary statistics 60 -- 3.4 Estimates of the central tendency 61 -- 3.5 Estimates of variation 69 -- 3.6 Coefficient of variation 74 -- 3.7 Confidence limits 75 -- 3.8 Parametric and non-parametric data 76 -- 3.9 Transforming data 80 -- 4 An introduction to hypothesis testing -- 4.1 What are the hypotheses to be tested? 83 -- 4.2 Working out the calculated value of the test statistic 93 -- 4.3 Finding the critical value of the test statistic 95 -- 4.4 The rule 106 -- 4.5 What does this mean in real terms? 107 -- 5 Hypothesis testing: Do my data fit an -- expected ratio? -- 5.1 Chi-squared goodness of fit test 111 -- 5.2 Heterogeneity in a goodness of fit test 119 -- 5.3 Chi-squared test for association 123 -- 5.4 Chi-squared test with one degree of freedom 127 -- 5.5 G tests 133 -- 5.6 The problem with small numbers 137 -- 5.7 Experimental design, and chi-squared and G tests 139 -- 6 Hypothesis testing: Associations and -- relationships -- 6.1 Correlations 147 -- 6.2 Spearman's rank correlation 149 -- 6.3 Pearson's product moment correlation 155 -- 6.4 Regressions 158 -- 6.5 Model I: Simple linear regression 160 -- 6.6 Model II: Principal axis regression 166 -- 6.7 Model II: Ranged principal axis regression 172 -- 6.8 About regression lines 177 -- 6.9 Experimental design, correlation, and regression 178 -- 7 Hypothesis testing: Do my samples come from -- the same population? Parametric data -- 7.1 z test with unmatched data 182 -- 7.2 t test (Student's t test) for unmatched samples 187 -- 7.3 z and t tests for matched data 190 -- 7.4 Introduction to parametric analysis of variance (ANOVA) 194 -- 7.5 Parametric one-way ANOVA with equal numbers of replicates 197 -- 7.6 Tukey's test following a parametric one-way ANOVA 201 -- 7.7 Parametric two-way ANOVA with equal numbers of replicates 205 -- 7.8 Tukey's test following a parametric two-way ANOVA 210 -- 7.9 Two-way nested parametric ANOVA with equal replicates 212 -- 7.10 Factorial three-way parametric ANOVA 218 -- 7.11 Factorial three-way parametric ANOVA with replicates 225 -- 7.12 Experimental design and parametric statistics 226 -- 8 Hypothesis testing: Do my samples come -- from the same population? Non-parametric data -- 8.1 Mann-Whitney U test 235 -- 8.2 Wilcoxon's matched pairs test 238 -- 8.3 One-way non-parametric ANOVA (Kruskal-Wallis test): -- General hypotheses 241 -- 8.4 One-way non-parametric ANOVA: Specific hypotheses 248 -- 8.5 Two-way non-parametric ANOVA: General hypotheses 248 -- 8.6 Two-way non-parametric ANOVA: Specific hypotheses 256 -- 8.7 Scheirer-Ray-Hare test 258 -- 8.8 Experimental design and non-parametric tests of hypotheses 260 -- 9 Research, the law, and you -- 9.1 About the law 265 -- 9.2 Health and safety 267 -- 9.3 Access and sampling 278 -- 9.4 Animal welfare 288 -- 9.5 Working with humans 291 -- 9.6 Discussion topics 298 -- 10 Reporting your research -- 10.1 General format 302 -- 10.2 Title 303 -- 10.3 Abstract 303 -- 10.4 Keywords 304 -- 10.5 Acknowledgements 305 -- 10.6 Introduction 305 -- 10.7 Method 308 -- 10.8 Results 312 -- 10.9 Discussion 326 -- 10.10 References 328 -- 10.11 Appendix 334 -- 10.12 Your approach to writing a report 334.
Includes index.
Bibliography: p. [378]
Keywords: Life sciences -- Methodology; Research