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Statistical Analyses and Results: An Overview From gender equity and the sharing of obituary space we turn to gender equity in the identification of the deceased. In the following three chapters we investigate patterns in the naming of the deceased through the analysis of personal names, titles, and occupations. The identification of the deceased in the obituary pages, like that of people in the world outside them, is constructed through linguistic forms appropriately chosen according to context. Such forms would include not only the individuals’ names but at times their titles and occupations as well. Not all, however, are always necessary. The choice is often dependent on the individual and on the context within which the identification is made. Context is defined broadly to include conversations vis- à-vis written texts, degrees of formality, and other sociocultural and linguistic considerations. We assume that all people would be identified by their names, by their titles within certain cultures depending on the situational context, and by occupation also according to context. But the questions we ask in the next three chapters are about the identification of the deceased in the obituary pages: to what extent does it conform to or diverge from expected norms of identification as we know them within and across cultures, and to what extent does context (obituary space) affect this identification? To establish patterns in the identification of the deceased and determine the degree of significance they may attain, I rely on quantitative measures of distribution. These are discussed briefly in this overview within the context of the various analyses that have been applied to the data, the overall statistical results, and the way they are reported in the next three chapters. I have relied on statistical results from the contingency table analysis as it is performed on Statview Statistics software. A number of statistical tests of significance are reported in that analysis. I have relied mostly on the chi-square test of significance to determine the effect each independent variable (Sex, Culture, and Time) may have on each of the three linguistic variables (Name, Title, and Occupation), more specifically, the probability that the distribution 109 110 Statistical Analyses and Results of the population resulting from such combinations is significantly different from normal variation. To measure the strength of the association between each pair, I have relied on Phi or Cramer V analysis depending on which is reported through the contingency table analysis. The tables of statistics (appendix B) report actual numbers and percentages, results of the chi-square test of significance (χ2), and level of significance achieved (p-value). Where relevant, results of the analysis of actual and expected values are also reported in the discussion, as was done in chapter 2. In the tables of statistics, I have used boldface to indicate significant overrepresentation and underscore for significant underrepresentation of a group. In view of the relatively large number of variables involved—three major linguistic variables to be analyzed by three independent variables—a number of data sets have to be reported. To avoid producing a large number of tables and to allow the reader to locate the results in one area, at least for comparative purposes, I have combined the results and reported them in single tables for all linguistic variables according to the type of combinatorial analysis involved and I have included them all in appendix B. Results from the analysis of social and professional titles are also included in the tables and in the figures presented in this overview for completeness and space considerations, but they should be viewed as subtypes of Title to be discussed in chapter 4. It is for this reason that they are listed in the tables and graphs of this section after Occupation, to remind the reader that the first three listed and presented (Name, Title, Occupation) are the three major linguistic variables and that including social and professional titles with them here is just a convenience. A number of combinations or alternative analyses of the data are available to test for effects between each pair of variables (dependent and independent). First is the analysis of each set of linguistic data for significant independent effect from Sex, Culture, and Time. One purpose of this overview is to determine if the distribution of the linguistic data by each of the three variables is significantly different from the expected distribution...


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