Statistics for Anthropology

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  • Edition: 2nd
  • Format: Paperback
  • Copyright: 2012-04-09
  • Publisher: Cambridge University Press
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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.

Table of Contents

List of partial statistical tablesp. xi
Prefacep. xiii
Introduction to statistics and simple descriptive statisticsp. 1
Statistics and scientific enquiryp. 1
Basic definitionsp. 3
Variables and constantsp. 3
Scales of measurementp. 4
Accuracy and precisionp. 6
Independent and dependent variablesp. 6
Control and experimental groupsp. 7
Samples and statistics, populations and parameters. Descriptive and inferential statistics. A few words about samplingp. 8
Statistical notationp. 9
Chapter 1 key conceptsp. 12
Chapter 1 exercisesp. 12
The first step in data analysis: summarizing and displaying data. Computing descriptive statisticsp. 13
Frequency distributionsp. 13
Frequency distributions of discontinuous numeric and qualitative variablesp. 13
Frequency distributions of continuous numeric variablesp. 15
Stem-and-leaf displays of datap. 17
Graphing datap. 18
Bar graphs and pie chartsp. 19
Histogramsp. 21
Polygonsp. 21
Box plotsp. 21
Descriptive statistics. Measures of central tendency and dispersionp. 25
Measures of central tendencyp. 26
Measures of variationp. 29
Chapter 2 key conceptsp. 39
Computer resourcesp. 40
Chapter 2 exercisesp. 40
Probability and statisticsp. 42
Random sampling and probability distributionsp. 43
The probability distribution of qualitative and discontinuous numeric variablesp. 44
The binomial distributionp. 46
The Poisson distributionp. 48
Bayes' theoremp. 53
The probability distribution of continuous variablesp. 57
z scores and the standard normal distribution (SND)p. 63
Percentile ranks and percentilesp. 71
The probability distribution of sample meansp. 73
Is my bell shape normal?p. 77
Chapter 3 key conceptsp. 78
Computer resourcesp. 79
Chapter 3 exercisesp. 80
Hypothesis testing and estimationp. 83
Different approaches to hypothesis testing and estimationp. 83
The classical significance testing approachp. 83
The maximum likelihood approachp. 84
The Bayesian approachp. 84
Estimationp. 84
Confidence limits and confidence intervalp. 85
Point estimationp. 89
Hypothesis testingp. 90
The principles of hypothesis testingp. 90
Errors and power in hypothesis testingp. 93
Hypothesis tests using z scoresp. 98
One-and two-tailed hypothesis testsp. 100
Assumptions of statistical testsp. 101
Hypothesis testing with the t distributionp. 103
Hypothesis tests using t scoresp. 104
Reporting hypothesis testsp. 105
The classical significance testing approach. A conclusionp. 106
Chapter 4 key conceptsp. 106
Chapter 4 exercisesp. 107
The difference between two meansp. 108
The un-paired t testp. 108
Assumptions of the un-paired t testp. 112
The comparison of a single observation with the mean of a samplep. 116
The paired t testp. 117
Assumptions of the paired t testp. 119
Chapter 5 key conceptsp. 120
Computer resourcesp. 120
Chapter 5 exercisesp. 121
The analysis of variance (ANOVA)p. 122
Model I and model II ANOVAp. 122
Model I, one-way ANOVA. Introduction and nomenclaturep. 123
ANOVA assumptionsp. 131
Post-hoc testsp. 132
The Scheffé testp. 133
Model I, two-way ANOVAp. 135
Other ANOVA designsp. 143
Chapter 6 key conceptsp. 144
Computer resourcesp. 145
Chapter 6 exercisesp. 145
Non-parametric tests for the comparison of samplesp. 146
Ranking datap. 147
The Mann-Whitney U test for a two-sample un-matched designp. 148
The Kruskal-Wallis for a one-way, model I ANOVA designp. 153
The Wilcoxon signed-ranks test for a two-sample paired designp. 159
Chapter 7 key conceptsp. 164
Computer resourcesp. 164
Chapter 7 exercisesp. 164
The analysis of frequenciesp. 166
The X2 test for goodness-of-fitp. 166
The Kolmogorov-Smirnov one sample testp. 170
The X2 test for independence of variablesp. 172
Yates' correction for continuityp. 175
The likelihood ratio test (the G test)p. 176
Fisher's exact testp. 178
The McNemar test for a matched designp. 183
Tests of goodness-of-fit and independence of variables. Conclusionp. 184
The odds ratio (OR): measuring the degree of the association between two discrete variablesp. 185
The relative risk (RR): measuring the degree of the association between two discrete variablesp. 188
Chapter 8 key conceptsp. 190
Computer resourcesp. 190
Chapter 8 exercisesp. 191
Correlation analysisp. 193
The Pearson product-moment correlationp. 193
Non-parametric tests of correlationp. 199
The Spearman correlation coefficient rsp. 199
Kendall's coefficient of rank correlation-tau (¿)p. 202
Chapter 9 key conceptsp. 208
Chapter 9 exercisesp. 208
Simple linear regressionp. 209
An overview of regression analysisp. 210
Regression analysis step-by-stepp. 214
The data are plotted and inspected to detect violations of the linearity and homoscedasticity assumptionsp. 214
The relation between the X and the Y is described mathematically with an equationp. 215
The regression analysis is expressed as an analysis of the variance of Yp. 215
The null hypothesis that the parametric value of the slope is not statistically different from 0 is testedp. 217
The regression equation is used to predict values of Yp. 217
Lack of fit is assessedp. 219
The residuals are analyzedp. 221
Transformations in regression analysisp. 225
Chapter 10 key conceptsp. 232
Computer resourcesp. 232
Chapter 10 exercisesp. 232
Advanced topics in regression analysisp. 234
The multiple regression modelp. 234
The problem of multicollinearity/collinearityp. 235
The algebraic computation of the multiple regression equationp. 236
An overview of multiple-regression-model buildingp. 240
Dummy independent variablesp. 247
An overview of logistic regressionp. 251
Writing up your resultsp. 255
Chapter 11 key conceptsp. 255
Computer resourcesp. 256
Chapter 11 exercisesp. 256
Referencesp. 257
Indexp. 260
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