Statistics for Anthropology,9780521147088

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Statistics for Anthropology

by Lorena Madrigal

Edition:

2nd

ISBN13:

9780521147088

ISBN10:

0521147085

Format:

Paperback

Pub. Date:

4/9/2012

Publisher(s):

Cambridge University Press

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Summary

Anthropology as a discipline is rapidly becoming more quantitative, and anthropology students are now required to develop sophisticated statistical skills. This book provides students of anthropology with a clear, step-by-step guide to univariate statistical methods, demystifying the aspects that are often seen as difficult or impenetrable. Explaining the central role of statistical methods in anthropology and using only anthropological examples, the book provides a solid footing in statistical techniques. Beginning with basic descriptive statistics, this new edition also covers more advanced methods such as analyses of frequencies and variance, simple and multiple regression analysis with dummy and continuous variables. It addresses commonly encountered problems such as small samples and non-normality. Each statistical technique is accompanied by clearly worked examples and the chapters end with practice problem sets. Many of the datasets are available for download at www.cambridge.org/9780521147088.

Author Biography

Lorena Madrigal is Professor of Anthropology at the University of South Florida, Tampa. A biological anthropologist, she is particularly interested in the evolution of Afro and Indo Costa Rican populations residing in the Atlantic coast of Costa Rica. She is currently President of the American Association of Physical Anthropologists.

List of partial statistical tables	p. xi
Preface	p. xiii
Introduction to statistics and simple descriptive statistics	p. 1
Statistics and scientific enquiry	p. 1
Basic definitions	p. 3
Variables and constants	p. 3
Scales of measurement	p. 4
Accuracy and precision	p. 6
Independent and dependent variables	p. 6
Control and experimental groups	p. 7
Samples and statistics, populations and parameters. Descriptive and inferential statistics. A few words about sampling	p. 8
Statistical notation	p. 9
Chapter 1 key concepts	p. 12
Chapter 1 exercises	p. 12
The first step in data analysis: summarizing and displaying data. Computing descriptive statistics	p. 13
Frequency distributions	p. 13
Frequency distributions of discontinuous numeric and qualitative variables	p. 13
Frequency distributions of continuous numeric variables	p. 15
Stem-and-leaf displays of data	p. 17
Graphing data	p. 18
Bar graphs and pie charts	p. 19
Histograms	p. 21
Polygons	p. 21
Box plots	p. 21
Descriptive statistics. Measures of central tendency and dispersion	p. 25
Measures of central tendency	p. 26
Measures of variation	p. 29
Chapter 2 key concepts	p. 39
Computer resources	p. 40
Chapter 2 exercises	p. 40
Probability and statistics	p. 42
Random sampling and probability distributions	p. 43
The probability distribution of qualitative and discontinuous numeric variables	p. 44
The binomial distribution	p. 46
The Poisson distribution	p. 48
Bayes' theorem	p. 53
The probability distribution of continuous variables	p. 57
z scores and the standard normal distribution (SND)	p. 63
Percentile ranks and percentiles	p. 71
The probability distribution of sample means	p. 73
Is my bell shape normal?	p. 77
Chapter 3 key concepts	p. 78
Computer resources	p. 79
Chapter 3 exercises	p. 80
Hypothesis testing and estimation	p. 83
Different approaches to hypothesis testing and estimation	p. 83
The classical significance testing approach	p. 83
The maximum likelihood approach	p. 84
The Bayesian approach	p. 84
Estimation	p. 84
Confidence limits and confidence interval	p. 85
Point estimation	p. 89
Hypothesis testing	p. 90
The principles of hypothesis testing	p. 90
Errors and power in hypothesis testing	p. 93
Hypothesis tests using z scores	p. 98
One-and two-tailed hypothesis tests	p. 100
Assumptions of statistical tests	p. 101
Hypothesis testing with the t distribution	p. 103
Hypothesis tests using t scores	p. 104
Reporting hypothesis tests	p. 105
The classical significance testing approach. A conclusion	p. 106
Chapter 4 key concepts	p. 106
Chapter 4 exercises	p. 107
The difference between two means	p. 108
The un-paired t test	p. 108
Assumptions of the un-paired t test	p. 112
The comparison of a single observation with the mean of a sample	p. 116
The paired t test	p. 117
Assumptions of the paired t test	p. 119
Chapter 5 key concepts	p. 120
Computer resources	p. 120
Chapter 5 exercises	p. 121
The analysis of variance (ANOVA)	p. 122
Model I and model II ANOVA	p. 122
Model I, one-way ANOVA. Introduction and nomenclature	p. 123
ANOVA assumptions	p. 131
Post-hoc tests	p. 132
The Scheffé test	p. 133
Model I, two-way ANOVA	p. 135
Other ANOVA designs	p. 143
Chapter 6 key concepts	p. 144
Computer resources	p. 145
Chapter 6 exercises	p. 145
Non-parametric tests for the comparison of samples	p. 146
Ranking data	p. 147
The Mann-Whitney U test for a two-sample un-matched design	p. 148
The Kruskal-Wallis for a one-way, model I ANOVA design	p. 153
The Wilcoxon signed-ranks test for a two-sample paired design	p. 159
Chapter 7 key concepts	p. 164
Computer resources	p. 164
Chapter 7 exercises	p. 164
The analysis of frequencies	p. 166
The X² test for goodness-of-fit	p. 166
The Kolmogorov-Smirnov one sample test	p. 170
The X² test for independence of variables	p. 172
Yates' correction for continuity	p. 175
The likelihood ratio test (the G test)	p. 176
Fisher's exact test	p. 178
The McNemar test for a matched design	p. 183
Tests of goodness-of-fit and independence of variables. Conclusion	p. 184
The odds ratio (OR): measuring the degree of the association between two discrete variables	p. 185
The relative risk (RR): measuring the degree of the association between two discrete variables	p. 188
Chapter 8 key concepts	p. 190
Computer resources	p. 190
Chapter 8 exercises	p. 191
Correlation analysis	p. 193
The Pearson product-moment correlation	p. 193
Non-parametric tests of correlation	p. 199
The Spearman correlation coefficient r_s	p. 199
Kendall's coefficient of rank correlation-tau (¿)	p. 202
Chapter 9 key concepts	p. 208
Chapter 9 exercises	p. 208
Simple linear regression	p. 209
An overview of regression analysis	p. 210
Regression analysis step-by-step	p. 214
The data are plotted and inspected to detect violations of the linearity and homoscedasticity assumptions	p. 214
The relation between the X and the Y is described mathematically with an equation	p. 215
The regression analysis is expressed as an analysis of the variance of Y	p. 215
The null hypothesis that the parametric value of the slope is not statistically different from 0 is tested	p. 217
The regression equation is used to predict values of Y	p. 217
Lack of fit is assessed	p. 219
The residuals are analyzed	p. 221
Transformations in regression analysis	p. 225
Chapter 10 key concepts	p. 232
Computer resources	p. 232
Chapter 10 exercises	p. 232
Advanced topics in regression analysis	p. 234
The multiple regression model	p. 234
The problem of multicollinearity/collinearity	p. 235
The algebraic computation of the multiple regression equation	p. 236
An overview of multiple-regression-model building	p. 240
Dummy independent variables	p. 247
An overview of logistic regression	p. 251
Writing up your results	p. 255
Chapter 11 key concepts	p. 255
Computer resources	p. 256
Chapter 11 exercises	p. 256
References	p. 257
Index	p. 260
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