Targeting Women Donors: A Response to “Women and Human Services Giving”

Women and Human Services Giving" by Jerry D. Marx (January 2000, pp. 27-38) addressed an important topic for social services organization fundraisers. Marx's thorough descriptions and discussion regarding the issue's importance, historical background, past research, and method were extremely beneficial. However, I was puzzled by his interpretations of some of the study's results. First the total respondents in the logistic regression model predicting human service giving was 293 (see Table 3, p. 33). However, the total sample was 2,719 (see Table 1, p. 31). The author neither observed nor explained why there was such a large discrepancy between the two. Perhaps such a discrepancy was due to the model's exclusion of cases with missing data. The respondents in the logistic regression model consisted of only 11 percent of the respondents in the sample. Are the 2,426 respondents excluded from the logistic regression model analysis systematically different from these 293 respondents in any way? Can the 293 respondents really represent the 2,719 respondents? Second, Marx stated, "Income was defined as total household income, before taxes, for the year 1995. The variable coding ranged from 1 for 'under $5,000' to 18 for the income category, '$125,000 or more.' Again, Table 3 shows that those with higher incomes were about 10 percent more likely to give to human services, when other factors were controlled" (p. 33). I feel that Marx's statement might be somewhat misleading. The findings should be explained differently, perhaps: The odds of a person having donated increased by about 10 percent with each additional category of income. Therefore, for example, the odds of an income category 2 respondent having donated was 10 percent higher than that of an income category 1 respondent, and the odds of an income category 15 respondent having donated was more than 90 percent higher than that of an income category 6 respondent (that is, each 10 percent compounding to the next higher unit). Third, the logistic regression model results (see Table 3, p. 33) indicated only four variables that were significant in predicting whether a person is likely to donate, including: "gender," "race," "total annual income," and being a "human services volunteer." The variables of "empowerment" and "organizational commitment" were not significant predictors, according to the logistic regression model. The model further suggested that there were no interaction effects between gender and other independent variables (including "empowerment" and "organizational commitment"). Therefore, it is fairly legitimate to say that the variables of "empowerment" and "organizational commitment" cannot predict donating either among men or women. Notwithstanding this statement, Marx emphasized in his section "Implications for Social Work" the importance of "empowerment" ("It is in my power to do things that improve the welfare of others") and "organizational commitment" ("generally, charitable organizations play a major role in making our communities better places to live"), along with being a "human service volunteer." He suggested that [t]hese findings can be used by social workers in fundraising to develop strategies that target appeals to women that meet this profile. More specifically, in terms of recommended strategy, social workers should consider the following. First, ... an important motivation for women donors indicated in this [study] ... is a concern for empowerment. (p. 34) According to the logistic regression model, "empowerment" is not a significant predictor for either men or women. The reason Marx emphasized "empowerment" and "organizational commitment" was based on his chi-square analysis results, comparing female with male donors. He grouped "asked to give," "affordability," "organizational commitment," "religious involvement," "empowerment," and so forth into "motivations for human service contributors" (p. …