AMERICAN ANTHROPOLOGIST ⢠VOL. 101, No. 3 ⢠SEPTEMBER 1999 between staple goods and wealth-based political economies. His overview compares ...
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AMERICAN ANTHROPOLOGIST
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VOL. 101, No. 3
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SEPTEMBER 1999
between staple goods and wealth-based political economies. His overview compares developments in prehistoric economies in North America, the Andes, and Oceania, highlighting the ways in which different parts of Oceania are distinctive, and asks why they do not share the evolutionary trajectory represented in the continental areas. As a whole this volume showcases some of the best archaeological research being done in Oceania on the transport of materials and variation in functionally based interaction. A number of important issues are raised in this volume, only some of which are sufficiently addressed. While the volume title identifies interaction as the topic, most papers explore exchange and/or the transport of resources and artifacts. Interaction here is used in its functional sense, not its traditional archaeological meaning where it is associated with cultural transmission and style. This volume illustrates why archaeologists need multiple samples of basalts (or other materials) from source outcrops, quarries, and archaeological contexts in order to address questions of measurement accuracy and geochemical overlap among differently scaled units. Also, if archaeologists expect to quantitatively
scale transport/interaction, geochemically based units must be linked to some set of archaeologically based classes. Archaeological materials and/or site components also need to be dated so that temporal variation can be identified. The strengths of the volume are those we come to expect among archaeologists working in this region: cooperation among researchers and institutions, the interplay between interpretive models and analysis, and the quality of geological research. Finally, one of the findings—perhaps unintended—of the research reported in this volume is the degree of prehistoric interaction in the Pacific, particularly Polynesia. Although the spatial scale of functionally based interaction was obviously delimited by geographic factors, interaction was variable over space and time, remote places with limited land areas were at risk when inter-archipelago interaction began to decline, and in no case was isolation complete. Archaeologists must now ask what effects these findings have on traditional views of relatedness formed from the product of contemporary trait distributions.
General Anthropology Statistics for Anthropology. Lorena Madrigal. Cambridge: Cambridge University Press, 1998. 238 pp. DWIGHT READ
University of California—Los Angeles This disappointing, if not unacceptable, textbook was written by an anthropologist who, through her teaching of courses on statistical methods for anthropologists, is cognizant of the problems anthropology students have with most statistics textbooks. The initial impression of a reasonable balance between using formalism to present concepts and a "cookbook" approach to presenting formulas is belied upon more detailed reading. Numerous errors in its presentation of statistical concepts make use of Statistics for Anthropology as a textbook for a course problematic, if not impossible. On the positive side, Dr. Madrigal's writing style is easy to read and she includes topics motivated by reference to anthropological applications. The author moves easily between formulas and illustrative examples from the anthropological literature. When presenting formulas the author first discusses the meaning of terms in simple English, then illustrates computation of a formula in a detailed, step-by-step manner, and lastly works through a "Practice problem." Exercises are provided at the end of each chapter (though somewhat limited in number), along with answers to selected exercises. Dr. Madrigal also shows how the SAS Assist statistical package can be used to supplement the book. In terms of content, the book begins with an overview chapter of statistic methodology, including a short discussion of the distinction between a statistical population and a sample. In chapter 2 Madrigal discusses frequency distributions and graphs. This sets the stage for the next chapter on descriptive statistics and
measures of central tendency and dispersion. In the following chapter, she discusses how the proportions in afrequencydistribution become probabilities under random sampling. This serves as an introduction to the normal distribution and z-scores as providing the means to determine the probability of getting a specified range of values for a variable when doing random sampling of a population for which the variable of interest has anormal distribution. Next, hypothesis testing is introduced, including a brief discussion of the power of a statistical test. The f-distribution is also introduced in this chapter. In the next two chapters, Madrigal discusses the r-test (including both paired and unpaired Mests) for comparing the means for two populations and introduces ANOVA and the /-test. Non-parametric tests (Mann-Whitney U test, Kruskal-Wallis test, and the Wilcoxon signed-rank test) are discussed in the subsequent chapter. The next two chapters consider linear regression and correlation analysis, respectively, and the last chapter introduces the^ 2 test for goodness-of-fit and its use in the analysis of two-way contingency tables. My disappointment with the book stems from acts both of omission and commission. A primary omission is the Central Limit Theorem, without which there is no basis for asserting why normality should characterize certain frequency distributions. For the distribution of sample means, for example, instead of referring to the Central Limit Theorem with its implication that sample means tend to have a normal distribution, the author incorrectly asserts that "the distribution of the sample means is normal" (p. 70, emphasis added) and later when discussing regression analysis she remarks that "the remaining variation of the Y [i.e., the residuals] should have a normal distribution, just like a random data set would" (p. 166). What constitutes a "random data set" is not explained, and it is difficult to think of any interpretation of that phrase that would justify the claim about
BOOK REVIEWS
normality of the residuals without reference to the Central Limit Theorem. Whereas in the earlier part of the text Madrigal discusses concepts underlying formulas, by the time linear regression and correlation analysis are reached the exposition shifts to "handwaving" and "cookbook" writing. For example, rather than discussing the ideas underlying least squares regression analysis, she merely comments "The line that we want is the line that is closest to all points at once, and is the one obtained with the 'least-squares' technique" (p. 152, emphasis in the original), but the regression line is not "closest to all points at once" and no clarification of what is meant by the "least-squares" technique is provided. Substantive errors abound. The author confounds a regression model with an estimated regression equation when she writes: "The equation that describes this line in the sample will be of the form ¥ = a + b (X) + £ where y (yhat) is the predicted value of Y.. ."(pp. 152-153, emphasis in the original) and then compounds this confusion when she states, erroneously, that "[t]he parametric equation [is] . . . Y = a + fi(X) where Y is known without error, hence ... the lack of the error term" (p. 154, emphasis added). The formulas for computation of the estimated slope and intercept are presented without indication of what the terms in a formula measure, and the relationship between the sign of the slope and the directionality of the relationship is not mentioned. Later, under correlation analysis, the directionality of X in relationship to Yis provided only for correlations where ris close to +1 or - 1 , leaving it to the reader to infer what happens to directionality for other values of r. The author seems to be unaware of situations where one might want to test the null hypothesis about the y intercept, HO: a = 0, since she informs the reader that "[t]his hypothesis is generally of no interest for the purpose of regression analysis" (p. 174). Dr. Madrigal neither brings out the relationship between the correlation coefficient p, and the slope j3, in regression analysis, nor the fact that for a pair of variables the coefficient of determination R2 for linear regression is just the correlation coefficient squared. Yet she goes through great pains to illustrate in detail the computation of confidence intervals for linear regression analysis.
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Errors occur in other chapters as well. In the chapter on ANOVA it is erroneously stated that "By definition: SStotal = SSwithin + SSamong" (p. 115, emphasis added), an error repeated in the chapter on regression analysis (p. 159). When discussing the chi-square test, she comments that the difference between the expected value and the observed value must be squared when computing the%2 statistic "so that the positive and negative differences do not cancel each other out" (p. 194). The astute student might think it simpler to merely use the absolute value of the difference if making the difference a positive number were all that is needed. Other problematic statements include: (1) when discussing the sample variance it is stated that 'The quantity n - 1 is known as the degrees of freedom" (p. 44), but there is no explanation of what is meant by degrees of freedom; (2) computation of allele frequencies is used in an example without mentioning that the calculation assumes Hardy-Weinberg equilibrium (p. 58); (3) she asserts that "the curve [for the normal distribution] never actually touches the horizontal axis [because] 0.26% of all outcomes . . . are found to the right and left of both third standard deviations" (p. 62); (4) the claim is made that "The reader can determine the frequency of an outcome [for the normal distribution] by locating it in the horizontal line, then drawing a straight line up to the curve, and finding out the frequency pinpointed by the curve" (p. 64); (5) she fails to exclude one of the tails of the distribution when computing the probability of a Type II error (pp. 83-84); (6) she erroneously asserts that "[t]he main reason one-tailed tests are not frequently performed . . . is that such tests are associated with a greater probability of a type I error, even if the same a level is used" (p. 88,92); and (7) in one example a hypothesis is stated in terms of sample means rather than population means (p. 91). There are other problems, but space does not permit providing an exhaustive list. Some typographical errors were missed in the proofreading: page 44, s2 = Js2 should be s = 4s2 page 97, "means of size h" should by "means of samples of size h"; page 9S,df= nx + n2 should bedf= n\ + n2-2, andthetwotableson pages 174 and 175 with the same data have a pair of values that do not match.
Linguistic Anthropology Color Categories in Thought and Language. C. L Hardin and Luisa Maffi, eds. New York: Cambridge University Press, 1997. 404 pp. BARBARA A. C. SAUNDERS
University of Leuven, Belgium In 1992 the conference "Color Categories in Thought and Language" was held at the Asilomar Conference Center in California. Sponsored by Syracuse University and the National Science Foundation, its aim was to develop growing conceptual and institutional links between anthropological linguistics and
one branch of color science. The point of departure was Berlin and Kay's ambitious project Basic Color Terms ([1969]1991, henceforth BCT). In 1969 evidence had been presented for seven stages of evolutionary emergence of 11 semantic universals. Twenty language-mapping "experiments" producing a "clustering" of color foci had been interpreted as proof of universality (though as 15 of the 20 languages were at Evolutionary Stage Seven, the clustering was a foregone conclusion). Despite fundamental methodological flaws (Hickerson 1971), the anthropological community responded rapturously, predicting BCT would become one of the most remarkable discoveries of anthropology (Durbin 1972; Sahlins 1976), a " 'bio-cultural'
