Senior Thesis - Unpublished
A Quantitative Study of Gender, Educational Attainment, and Size of Community In the United States During the Years of 1978 and 1998
Al Youngblood
SOCI 3308, Quantitative Methods
Professor Toni Watts
May 3, 2004
Table of Contents
-
I. Introduction . . . . . . . . 1
II. Review of Literature . . . . . . . 1
III. Gaps in Literature . . . . . . . . 8
IV. Proposed Research Design . . . . . . 9
V. Results . . . . . . . . . 12
VI. Conclusion . . . . . . . . 15
VII. Bibliography . . . . . . . . 17
VIII. Appendix A: Chi-Square Tests . . . . . . 20
Table 1. DEG78 versus SEX78SMALL
Table 2. DEG78 versus SEX78MED
Table 3. DEG78 versus SEX78LARGE
Table 4. DEG98 versus SEX98SMALL
Table 5. DEG98 versus SEX98MED
Table 6. DEG98 versus SEX98LARGE
IX. Appendix B: ANOVA Tests . . . . . . 27
Table 7. ALLEDUC78 Analysis
Table 8. ALLEDUC98 Analysis
Introduction
Without question, there exists a monumental amount of research confirming that the distinction of gender accounts for, or is related to, differential access, process, and outcomes relative to men within economic, political, and social institutions (Kiecolt and Acock 1998; Sewell, Hauser, and Wolf 1980; DiMaggio and Mohr 1985). The study of gender encompasses an impressive swath of sub-disciplines, among which education and the schooling process have been studied extensively as a key socializing process (Bauch 2001). Typically left out of a standard examination of gender, however, is a sense of space, geography, and ecology that belie “community processes” in which socialization takes place (Herting, Grusky, and Van Rompaey 1997; Little and Panelli 2003).
These basic rationales guide the present research, and a new perspective engaging the variables of gender, education, and space is studied. Using selected data from the 1978 and 1998 General Social Survey (GSS), this paper tests the proposition that there exists a statistically-significant relationship between the size-population of a community in which a woman lives and her educational attainment, as measured by completion of high school, when compared with similarly-situated men.
Review of Literature
Before approaching any study of “urban” and “nonrural” or “rural” communities, reviewing overarching theoretical considerations latent within previous research is both instructive and informative (e.g., Shiang and Sewell 1980). According to Miller and Crader (1979), differences between larger and smaller social groupings, particularly in their form and effect, have traditionally been guided by several approaches within sociology:
- Tönnies’s distinction between Gemeinschaft (an organic, instinctive communal structure) and Gesellschaft (a purposive, organizational, goal-oriented structure);
- Durheim’s distinction between organizational tendencies to be either Mechanical (a rigid, undifferentiated form) and Organic (a differentiated, humanistic form); and
- Maine’s anthropological distinction of forms between Status (a rigid framework of ascribed-inherited power positions) and Contract (a variable, mutually-respective system).
Tönnies’s perspective heavily influenced Louis Wirth, who was among the first of the Chicago School of American urban sociologists, and, in turn, has shaped modern sociological considerations of ruality and urbanity (Kennedy and Krahn 1984; Miller and Crader 1979).
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Sociological Perspectives
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Rural versus Non-Rural
-
The distinction between “rural” and “nonrural” has typically been viewed as either a continuum relative to social life with loose, but demonstrable, demarcation (e.g., Miller and Crader 1979), or a strict gradation relying upon population/demographic definitions, as with the U.S. Census (e.g., Fasko and Fasko 1999; Lichter, Cornwell, Eggebeen 1993).
Intertwined with these differences are other salient considerations. Bauch (2001:209) describes specific normative differences between rural and nonrural contexts in agreements, discipline, “aesthetic quality,” relationships, and homogeneity. Fischer (1978) describes how urban and rural cultural differences flow and feedback from one another. Moreover, a community’s regional character should not be forgotten when evaluating “urbanness” and “ruralness” because “sociocultural and physiographic” spaces are not alike (Herting, Grusky, Van Rompaey 1997), nor are their geographic spaces similar (Little and Panelli 2003). Further still, researchers have pointed out a persistent theme within the literature that these rural/urban differences are “nil or soon to be nil” (Fischer 1978).
Indeed, no framework to distinguish nonrural from rural has been fully embraced (Perez 1979).
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Ruality, Education, and Gender
Several researchers have remarked that the topic of “rural educational outcomes” is “missing from the research literature” (Roscigno and Crowley 2001, citing Lichter, Cornwell, and Eggebeen 1993). Given that “education” is a socioeconomic variable (Sewell, Hauser, and Wolf 1980; McGrath, Swisher, Elder, and Conger 2001), and that there exists an overabundance of literature examining the relationship of education with dimensions of race, income, socioeconomic status, and gender, it is surprising that the literature is devoid of more studies examining the spatial relationship to “community size”. Only one study by Roscigno and Crowley (2001: 268) has “extend[ed] the literature by considering spatial variation in achievement/attainment across rural and nonrural locales.”
On a large scale, community size is economically significant when factoring a woman’s education in labor-force participation with concurrent expansion of national economic growth (Benavot 1989). On a small scale, the maintenance of local economic bases by educated women during economic restructuring is directly related to community size (Nelson and Smith 1998).
The literature is broadly divided into two overlapping sets, based upon their underlying intention or theme: “Affective Studies,” which provide functional descriptions of social/cultural processes of rural or urban life, and “Effective Studies,” which involve statistically-laden demography of rural or urban communities.
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Affective Studies
Typically, differences between urban/nonrural and rural areas are explained by divisive characteristics, such as “lack of opportunity” (Tickamyer and Duncan 1990; Roscigno and Crowley 2001; Elder 1963), “family structural arrangements” (Lichter, Cornwell, and Eggebeen 1993), “social capital” (Israel, Beaullieu, and Hartless 2001; Lichter, Cornwell, and Eggebeen 1993; DiMaggio and Mohr 1985), “status attainment” or “earnings” (McGrath, Swisher, Elder and Conger 2001; Shiang and Sewell 1980; Lowe and Pinhey 1980), “Migration” (Shiang and Sewell 1980; Herting, Grusky, and Van Rompaey 1997), and “rural baggage” (Kennedy and Krahn 1984; Fasko and Fasko 1999).
Two intriguing theoretical dimensions will be considered due to their inherent prevalence and applicability: “opportunity” and “capital”.
-
Opportunity
This line of research presumes a link between educational attainment and scarcity of opportunity resources and structures (Elder 1963) and has its roots in the work of Wirth and Park (Miller and Crader 1979; Roscigno and Crowley 2001). Opportunity theorists of education have emphasized the lack of economic means for advancement and mobility in local labor markets (Tickamyer and Duncan 1990; Roscigno and Crowley 2001), poorer schools and communities (Roscigno and Crowley 2001), and school-community integration/isolation (Bauch 2001), to explain lower overall educational attainment among rural versus nonrural youth. Opportunity theory is distinguished from “Status attainment” modeling which “may not apply well outside metropolitan populations” (McGrath, Swisher, Elder, and Conger 2001: 248).
Opportunity theories have been criticized for failing to take into account spatial considerations (e.g., rural versus urban), in addition to a failure to conceptualize and model both family and school, two key educational “institution spheres” (Roscigno and Crowley 2001).
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Social Capital
Coleman (1988:S100-101) explains “social capital” as a loose collection of “intangible” things, whether in the form of “habits,” “ideas,” or “knowledge,” that “facilitate certain actions of actors … within the structure” that they function, and, to that end, possess different valuation based on rational actors’ “use to achieve their interest.”
Extending this abstract concept to the family and education, thus, “family social capital” is the set of familial “norms, social networks, and relationships between adults and children that are valuable” (Israel, Beaulieu, and Hartless 2001)—which Lichter, Cornwell, and Eggebeen (1993:55-58) describe in depth (e.g., single-parent family, parental educational attainment, divorce, dual-earning occupations)—and that prepare or obstruct a student from scholastic achievement.
Bourdieu’s conception of “culture capital,” whose origin arises from his explanations of French educational stratification, has been instructive to American sociologists studying inequality and status differentiation in so far as culture capital is intrinsically tied to Coleman’s formulation of “social capital” (DiMaggio and Mohr 1985). Israel, Beaulieu, and Hartless (2001) have gone the farthest in developing a model of “family social capital” and educational attainment using the framework of “community field theory,” which maintains that economic differences between urban and rural communities are a reflection of different levels of “community” social capital.
Some of the main research conclusions in family social capital have been:
- Importance of poverty and extra-familial resources (Lichter, Cornwell, and Eggebeen 1993)
- Identification of “opportunity” within a student’s milieu (McGrath, Swisher, Elder, Conger 2001; Lowe and Pinhey 1980)
- Valuation of academic symbols (Israel, Beaulieu, and Hartless 2001).
Despite this rich toolset, Capital theories have been roundly criticized for their relative lack of sophistication and standardized modeling (Israel, Beaulieu, and Hartless 2001).
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Affective Studies
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Ecological Perspectives
The field of ecology has been important to American sociologists ever since Robert Park elucidated some basic ecological considerations; it has been studied, on way or another, under such headings as “Rural Sociology,” “Social Ecology,” “Urban Life,” “Ecology,” “Human Ecology,” or “Rural Ecology” (Perez 1979). Widely held sociological understandings of what defines “rural” involve the ecological dimensions of size, population, and isolation, all of which have some effect upon residential “cognitive and behavioral” differences, one of which is “satisfaction” of one’s community (Miller and Cradler 1979).
The only explicitly ecological framework that was found to examine the dynamics of rural and urban choices and outcomes was not meant for such purposes (Perez 1979). As such, ecological considerations should be understood and acknowledged (Miller and Crader 1979; Bauch 2001), but not necessarily quantized (not in agreement with Herting, Grusky, and Van Rompaey 1997).
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Geographic Perspectives
Following the establishment of a Study Group on Gender by the International Geographical Union in 1988, the field of geography has a renewed emphasis on the social dimension of gender, notwithstanding harsh criticism by feminist geographers of the earlier body of social geography as advancing “dominant ideologies” (Townsend 1990). The notion of “gender and the rural environment” is intriguing for its potential to understand “spatial-ambient” elements of the natural space, such as spatial distance versus community knowledge (e.g., Little and Panelli 2003), within which urban and rural women negotiate their lives.
Notwithstanding these geographic considerations and their potential on regional effects on attitudes and opinions (Weakliem and Biggert 1999; Little and Panelli 2003), the individual characteristics of age and education cannot be studied as such (Fischer 1978). Likewise, rural gendered geography is in a “state of economic transformation” or, according to others, a “crisis” due to rural economic development (Little and Panelli 1993:286). Geography of gender is still in a nascent academic state, distanced away from conventional sociological analysis, with a voice of its own (Townsend 1990). Like ecology, geography should be understood in the rural/nonrural context, rather than forming a hard and fast basis for inquiry (Bauch 2001).
Gaps in the Literature
While “[i]nterest in the influence of community size on the socioeconomic achievements of their residents has a long history in rural and urban sociology” (Shiang and Sewell 1980:185), gender has too often been left out of its focus. Gender is included in most standard sociological studies of education, even though general education literature ignores spatial considerations (Bauch 2001). Indeed, community size holds tremendous implications for the types and variety of people studied (Lichter, Cornwell, Eggebeen 1993).
Education is another standard sociological variable that lacks a fuller rural context. It is surprising that “rural folk” value education the same as their urban counterparts (Lowe and Pinhey 1980), yet they still achieve at lower education rates in comparison. Why would this be so, given the importance of education in modern capitalist societies? A study of rural and nonrural education is important because occupational mobility forms the basis of stratification and often perpetuates cycles of poverty (Sewell, Hauser, and Wolf 1980; Tickamyer and Duncan 1990).
By extending both gender and education, the question is obvious: Are there any particular educational differences between these rural men and women? Does the general sociological finding that women do not attain the same level of education as men—whether because of marriage (DiMaggio and Mohr 1985) or adherence to traditional attitudes (Kiecolt and Acock 1988)—still hold in a rural setting?
The present study aims to answer some of these questions by testing data sets from two cohort groups from 1978 and 1998, in turn augmenting the longitudinal-educational approach by Roscigno and Crowley (2001) and stratified-probability approach of Israel, Beaulieu, and Hartless (2001). Both studies are comprehensive and in-depth, but neither explicitly bases its comparisons solely upon a respondent’s gender. Both use a variety of advanced statistical models and operationalizations, but neither examines the “bare,” straightforward proposition that educational attainment is affected by the population of a respondent’s community.
The specific cluster hypothesis posed in this study is:
The educational attainment of a woman versus that of a man is correlated to the size of the community she lives in, regardless of whether the population of the community she lives in is—
- Less than or equal to 10,000 inhabitants;
- Between 10,000 and 100,000 inhabitants; or
- Greater than or equal to 100,000 inhabitants
One of the study’s main benefits is to increase the limited knowledge of gender within the field of rural education and to offer more direct means of correlating gender-education-community size, before engaging in sophisticated sociological analysis, so that other non-sociological researchers can have a starting point to use their own methods.
Proposed Research Design
Using data taken from the 1978 and 1998 General Social Survey (GSS), collected by the National Opinion Research Center (NORC) at the University of Chicago, secondary analysis is used to find whether a woman’s education attainment, as compared to men, depends upon the size of community in which she lives.
A. Sample
The GSS is the “most comprehensive source of public opinion information” in the US (Weakliem and Biggert 1999); however, within the field of education, the National Educational Longitudinal Study (NELS)—also collected at the NORC—is preferred (Israel, Beaulieu, and Hartless 2001). Other data sets used within the literature include the National Longitudinal Survey (NLS), Common Core Data (CCD), Current Population Survey (CPS). The GSS is a cross-sectional sample of non-institutionalized English speaking persons aged 18 and over in the U.S. (Kiecolt and Acock 1988).
B. Variables
The data set in the present study was limited to 2,500 random cases (500 from each 5-year GSS period from 1978 and 1999) sufficient for statistical analysis (e.g., Scheer, Borden, Donnermeyer 2000). Variables with missing cases and responses of “Don’t Know” or “No Response” were respectively recoded for analysis. The 1998 standard GSS codebook was used for variable descriptions (Weakliem and Biggert 1999).
1. Size of Place, Gender, and Year
The size of place (“SIZE”) is the actual population during the interview, as determined when approximating to the nearest 1,000 of the smallest civil division listed by the U.S. Census. The respondent’s gender (“SEX”) and the date of the interview (“YEAR”) were coded by the interviewer.
Consistent with Scheer, Borden, and Donnermeyer (2000), SIZE was used to classify SEX into two groups of three:
1978 Data: (1) SEX78SMALL— Males and Females with populations less
than or equal to 10,000 inhabitants.
(2) SEX78MED— Males and Females with populations between,
but not including, 10,000 and 100,000 inhabitants.
(3) SEX78LARGE— Males and Females with populations greater
than or equal to 100,000 inhabitants
1998 Data: (1) SEX98SMALL— Males and Females with populations less
than or equal to 10,000 inhabitants.
(2) SEX98MED— Males and Females with populations between,
but not including, 10,000 and 100,000 inhabitants.
(3) SEX98LARGE— Males and Females with populations greater
than or equal to 100,000 inhabitants.
2. Education
Education was operationalized by using high school completion data, which is one measure of educational attainment from both a “social capital” perspective (Coleman 1988; Lichter, Cornwell, and Eggebeen 1993; Israel, Beaulieu, and Hartless 2001) and an opportunity perspective (Sewell, Hauser, and Wolf 1980). The highest level of degree (“DEGREE”) was recoded into a dichotomous variable of whether the respondent received or did not receive a high school diploma/GED, assuming that postsecondary degrees require at least a high school/GED diploma. Two variables, controlling for the years 1978 and 1998, respectively, were created: DEG78 and DEG98.
3. Method
Six chi-square tests were run using DEG78 versus SEX78SMALL, SEX78MED, SEX98LARGE, and DEG98 versus SEX98SMALL, SEX98MED, SEX98LARGE. See APPENDIX A. For some sense of comparison to the recoded DEG78 and DEG98, another ratio-level variable, level of education (“EDUC”), was introduced for the years 1978 and 1998 as “ALLEDUC78” and “ALLEDUC98” for two multivariate ANOVA tests. ALLEDUC78 and ALLEDUC98 did not distinguish between high school completion, just the respondent’s highest level of education. All testing was done using SPSS software.
This methodology retains advantages over other more sophisticated and time-consuming schemes (e.g., Roscigno and Crowley 2001) in that essential existence of relationships among gender-size-education is securely ascertained. Conversely, a major weakness lies in the unsophisticated operationalization of variables for the theoretical tasks attempted and a general lack of methodological rigor in relying upon simple dichotomous chi square tests.
Findings
Descriptive counts of male and female numbers and respective high school completion for population ranges of 1978 and 1998 are displayed in Table 1. It is clear that when comparing with males, females were likelier, as a percentage of their group numbers in 1978, not to complete high school (31% versus 25%), than in 1998, when females actually completed high school more so than did males (16% versus 18%). These completion effects have changed the most for women in lower and middle population ranges.
Year |
Size |
Number |
Total |
Completed |
Didn’t Complete HS |
|||||||
|
|
M |
F |
|
|
M |
F |
M |
F |
|||
1978
1998 |
Less than 10,000 |
85 |
106 |
191 |
60 |
67 |
25 |
39 |
||||
Between 10-100,000 |
73 |
106 |
179 |
57 |
74 |
16 |
32 |
|||||
Greater Than 100,000 |
53 |
77 |
130 |
41 |
58 |
12 |
19 |
|||||
Total |
211 |
289 |
500 |
158 |
199 |
53 |
90 |
|||||
Less than 10,000 |
78 |
83 |
161 |
62 |
66 |
16 |
17 |
|||||
Between 10-100,000 |
97 |
102 |
199 |
80 |
88 |
17 |
14 |
|||||
Greater than 100,000 |
55 |
84 |
139 |
47 |
72 |
8 |
12 |
|||||
|
Total |
230 |
269 |
499 |
189 |
226 |
41 |
43 |
||||
Table 1: Univariate Description of DEG78 & DEG98 for different SIZE-SEX
Bivariate analysis of the data set is collected in Table 2. For 1978 data, the probability of the chi-square statistic, respectively, was p=0.283, 0.220, 0.789, all greater than α=0.05. Thus, the null hypothesis that “in 1978, gender, controlled for three ranges of community size, is independent of educational attainment” is not rejected. Accordingly, the analysis concludes that there is not a relationship between educational attainments for women in three different types of communities in 1978.
Year |
Size |
Degrees of |
Chi-square (x2) |
P-Number |
1978
1998 |
Less than 10,000 |
1 |
1.153 |
0.283 |
Between 10-100,000 |
1 |
1.507 |
0.220 |
|
Greater Than 100,000 |
1 |
0.072 |
0.789 |
|
Less than 10,000 |
1 |
0.000 |
0.996 |
|
Between 10-100,000 |
1 |
0.546 |
0.460 |
|
Greater than 100,000 |
1 |
0.002 |
0.966 |
Table 2: Bivariate Analysis of DEG78 and DEG98
For 1998 data, the results are also the same. The probability of the chi-square
statistic, respectively, was p=0.996, 0.460, 0.966, all greater than α=0.05. Thus, the null hypothesis that “in 1998, gender, controlled for three ranges of community size, is independent of educational attainment” is not rejected. Accordingly, the analysis concludes that there is not a relationship between educational attainment for women in three different types of communities in 1978.
Multivariate analysis using ANOVA testing for 1978 and 1998 data is collected in Table 3. No significant relationship for p<0.05, when examining respective factors ALLEDUC78 and ALLEDUC98 with the different dependencies of SIZE-SEX, was found. Accordingly, any null hypothesis using ALLEDUC variables are not rejected. It is noted that the 1978 p-values of “less than 10,000” and “greater than 100,000” did approach an appreciable level of significance.
Year |
Size |
DF |
F-Number (F) |
Significance |
|
Dfw |
Dfb |
||||
1978
1998 |
Less than 10,000 |
173:17 |
1.516 |
0.094 |
|
Between 10-100,000 |
163:15 |
0.913 |
0.551 |
||
Greater Than 100,000 |
113:16 |
1.670 |
0.063 |
||
Less than 10,000 |
147:13 |
0.852 |
0.604 |
||
Between 10-100,000 |
181:15 |
0.948 |
0.512 |
||
Greater than 100,000 |
126:13 |
1.182 |
0.300 |
||
Table 3: AVOVA Analysis
In summary, both ANOVA and bivariate tests confirm that there is no significant relationship between DEG78/DEG98 variables and the various SEX-SIZE variables used within this study. All null hypotheses have been sustained. Several explanations might offer some insight.
The profound effect of family was not measurably distinguished from school, a chief criticism of prior research into spatial-education relationships (Roscigno and Crowley 2001; Israel, Beaulieu, and Hartless 2001). Family socioeconomic variance, a necessary sociological component when studying education (Shiang and Sewell 1980), also was not included. SES might have “masked” the subtle environmental effects.
Furthermore, sophisticated formulations for spatial attributes, far beyond the crude approximations used in the present study, exist within the literature. For example, when evaluating the “isolation” component of their measure of “community social capital,” Israel, Beaulieu, and Hartless (2001:51) found it necessary to control for three interrelated socio-spatial indicators: (1) county type, whose population density could “inhibit extensive networks of relationships,” (2) geographical homogeneity of student population, which reflects upon “opportunities for interaction with adult community members,” and (3) percentage of commuters, which correlates with lessened solidarity, community “activeness” and “mobilization.” They further describe a spatial-temporal component of “instability” to measure permanence of residential-structural ties.
In order to form a solid measure of ruality, and, in turn, reliably distinguish from nonrural/urban locales, researchers must “at the very least,” weigh adjacency pattern variations (e.g., the qualitative “feeling” of ruality versus suburbanity) using subjective data, such as school principal surveys about their school-community size (Roscigno and Crowley 2001) or “satisfaction” studies (Miller and Crader 1979).
Discussion and Conclusion
A spatiality component to address differences between residents of small and larger communities is difficult to construct, much less test, given that attitudes and culture flow interchangeably between these two domains; any test measure would necessarily need to be quite sophisticated in its implementation. (Fischer 1978). When performing such spatial analysis, mere census designations by population will not suffice (Roscigno and Crowley 2001:274-75).
This study, by using a “bare” comparison between gender and population versus educational attainment using GSS data from two dates, has shown that spatiality is not easily reducible into a straightforward comparative variable, like SES or race/ethnicity. Perhaps, spatial components best describe, but not necessarily control (e.g., Israel, Beaulieu, and Hartless 2001) the opportunity characteristics of local markets (i.e., limited employment, poverty) that structure familial association (i.e., proximity to grandparents, parental friends) and promote educational attainment through the presence of role models for encouragement and guidance (Bauch 2001). Indeed, Coleman (1988) believes that the capital totality of these arrangements predicts high school completion.
Further research would involve performing regression analysis using GSS variables like MAWORK (mother has worked more than a year), INCOME (yearly income), and SATCITY (city satisfaction) to control for rural family effects (i.e., income, parental education) that disadvantage educational attainment and institutional resource effects that limit educational potential (i.e., smaller tax base).
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