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Abstract

Have the core discussion networks of Americans changed in the past two decades? In 1985, the General Social Survey (GSS) collected the first nationally representative data on the confidants with whom Americans discuss important matters. In the 2004 GSS the authors replicated those questions to assess social change in core network structures. Discussion networks are smaller in 2004 than in 1985. The number of people saying there is no one with whom they discuss important matters nearly tripled. The mean network size decreases by about a third (one confidant), from 2.94 in 1985 to 2.08 in 2004. The modal respondent now reports having no confidant; the modal respondent in 1985 had three confidants. Both kin and non-kin confidants were lost in the past two decades, but the greater decrease of non-kin ties leads to more confidant networks centered on spouses and parents, with fewer contacts through voluntary associations and neighborhoods. Most people have densely interconnected confidants similar to them. Some changes reflect the changing demographics of the U.S. population. Educational heterogeneity of social ties has decreased, racial heterogeneity has increased. The data may overestimate the number of social isolates, but these shrinking networks reflect an important social change in America.
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AMERICAN SOCIOLOGICAL REVIEW, 2006, VOL. 71 (JUNE:353–375)
Social Isolation in America: Changes in Core Discussion Networks over Two Decades
Miller McPherson
University of Arizona and Duke University
Lynn Smith-Lovin
Duke University
Matthew E. Brashears
University of Arizona
APPENDIX TABLE OF CONTENTS
Table A1. Influence of Proportion Kin on Network Density and Heterogeneity
Table A2. Basic Parameters of Discussion Networks: 1985, 1987 and 2004
Table A3. Number of Respondents That Had Various Relationships with at Least One Discussion Partner
Table A4. Respondents with Spouses Only, Non-Spouse Kin, and Non-Kin in the Network
Table A5. Differences by Age, Education, Sex and Race in Network Size and Kin/Nonkin Composition
Table A6. Cooperativeness as Rated by the GSS Interviewer in 1985 and 2004
Table A7. Negative Binomial Regression of Size of Discussion Network
Table A8. Logistic Regression Coefficients for Having Someone to Talk to About Important Matters
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Table A1. Influence of Proportion Kin on Network Density and Heterogeneity (OLS Regression of Density and Heterogeneity on Proportion Kin)
Unstandardized Regression
Coefficient for Proportion Kin Predicted Value at Proportion Kin of R-squared
0 1
Dependent Variable 85 04 85 04 85 04 85 04
Density .36 .26 .42 .49 .78 .78 .18 .10
Age Heteregeneity (S.D. of alters’ ages) 5.48 7.53 7.30 6.56 13.54 13.88 .09 .15
Education Heterogeneity (S.D. of alters’
educations) .30 .20NS 1.61 1.38 1.92 1.58 .01 .001
Race Heterogeneity (IQV) -.08 -.12 .09 .15 .01 .03 .03 .04
Sex Heterogeneity (IQV) .27 .27 .54 .53 .79 .80 .06 .06
Note: All coefficients significant, p < .01, except where indicated.
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Table A2. Basic Parameters of Discussion Networks: 1985, 1987 and 2004
1985 (N=1531a) 1987 (N=1800) 2004 (N=1467b)
Network Size
0 10.0% 4.51% 24.6%
1 15.0% 14.27% 19.0%
2 16.2% 19.02% 19.2%
3 20.3% 49.06% 16.9%
4 14.8% 6.78% 8.8%
5 18.2% 3.20% 6.5%
6+ 5.4% 3.12% 4.9%
Mean 2.94 2.63 2.08
Mode 3 3 0
S.D. 1.79 1.28 1.81
Kin Network Size
0 26.4% 22.25% 42.4%
1 29.6% 35.70% 29.0%
2 21.6% 25.67% 14.5%
3 12.6% 16.39% 9.1%
4 6.3% 0.00% 3.5%
5c3.3% 0.00% 1.4%
Mean 1.44 1.36 1.12
Mode 1 1 0
S.D. 1.41 1.00 1.38
Non-kin Network Size
0 36.4% 36.79% 54.1%
1 22.2% 26.99% 21.3%
2 18.9% 20.03% 14.0%
3 13.0% 11.14% 5.7%
4 6.3% 3.50% 3.3%
5 3.3% 1.54% 1.5%
Mean 1.42 1.22 .88
Mode 1 0 0
S.D. 1.57 1.22 1.40
Proportion Kin
0 19.2% 18.57% 21.0%
.01-.33 15.4% 4.44% 10.9%
.34-.66 20.7% 25.71% 19.4%
.67-.99 14.5% 17.48% 11.6%
1 30.2% 33.80% 37.1%
Mean .53 .57 .60
S.D. .44 .38 .53
a Proportion kin is defined only for those who have at least one alter. Therefore, the N = 1395 for the proportion kin
analysis.
b N = 1070 for the analysis of proportion kin.
c Information on kinship was collected only on the first five alters cited. Therefore, the sum of the kin and non-kin alters is
not equal to the overall network size distribution.
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Table A3. Number of Respondents That Had Various Relationships with at Least One Discussion Partner (e.g. What
Percent of the Sample Mentioned A Spouse/Parent/etc. As A Person with Whom They Discussed
Important Matters?)
Type of Relationship to Respondentc1985 (N=1531) 1987a (N=1800) 2004b (N=1467)
Spouse 30.9% 43.21% 38.1%
Parent 23.6% 26.81% 21.1%
Sibling 21.1% 16.0% 14.11%
Child 17.9% 16.92% 10.19%
Other Family Member 18.2% 14.79% 11.8%
Co-worker 29.4% 28.44% 17.9%
Co-member of group 26.1% 21.48% 11.8%
Neighbor 18.5% 18.84% 7.8%
Friend 73.2% 77.98% 50.6%
Advisor 25.2% 45.03% 19.2%
Other 2.1% 4.50% 7.2%
a All differences between 1987 and 1985 are statistically significant at the p<.01 level except for Spouse, Parent,
Child, Friend, and Other, which are non-significant.
b All differences between 2004 and 1985 are statistically significant at the p<.01 level. Differences between 1987
and 2004 are statistically significant at the p<.01 level except for Siblings and Others, which are non-significant.
c Since more than one type of relationship can be mentioned for any given discussion partner (e.g., a co-worker can
also be a co-member of a group, an advisor and a friend), the percentages do not sum to 100.
Table A4. Respondents with Spouses Only, Non-Spouse Kin, and Non-Kin in the Network
1985 (N = 1531) 1987 (N=1800) 2004a (N = 1467)
Spouse ONLY Present 6.4% 5.5% 9.3%
At Least One Non-Spouse Kin 58.79% 60.19% 42.87%
At Least One Non-Kin 80.15% 83.79% 57.16%
a Significantly different from both 1987 and 1985 at the p < .01 level.
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Table A5. Differences by Age, Education, Sex and Race in Network Size and Kin/Nonkin Compositiona (Unstandardized OLS Regression Coefficients of
Network Variables on Respondents’ Demographic Characteristics)
Dependent Variables
Network Size # of Kin # of Non-Kin Proportion Kin
Independent Variables 85 87 04 85 87 04 85 87 04 85 87 04
A. Age
Age .02NS .02 NS .02 NS -.02 NS .02 .01 NS .03 -.01 NS .00 NS -.01 .01 .00 NS
Age2-.00 -.00 -.00 NS .00 NS -.00 -.00 NS -.00 .00 NS -.00 NS .00 -.00 NS -.00 NS
Constant 3.17 2.47 1.68 2.06 .97 .85 NS 1.17 1.51 .88 NS .69 .40 .48 NS
R .27 .14 .03 NS .08 .09 .04 NS .26 .08 .01 NS .18 .07 NS .05 NS
B. Educa tion
Sex
Educ (yrs) .19 .10 .14 .02 .02 .05 .15 .08 .08 -.03 -.01 -.02
Constant .61 1.33 .08 1.15 1.17 .45 -.47 .20 NS -.28 .87 .75 .79
R .35 .27 .23 .06 .05 .11 .35 .21 .20 NS .23 .12 .10
C.
Sex (f=1) -.04 NS .11 NS .20 NS .28 .17 .23 -.30 -.07 NS -.02 NS .07 .04 .01 NS
Constant 2.94 2.56 1.95 1.28 1.09 1.00 1.59 1.33 .89 .49 .50 .59
R .01 NS .04 NS .05 NS .10 .09 .03 .10 .03 NS .01 NS .10 .06 NS .01 NS
D. Race/ethnic (White is
reference category)
Black -.79 -.51 -.51 -.58 -.34 -.53 -.19 NS -.15 NS -.12 NS -.08 NS -.05 NS -.08 NS
Other -.43 NS -.06 NS -.64 -.42 -.03 NS -.49 .00 NS -.00 NS -.11 NS -.08 NS -.02 NS -.11
Constant 3.03 2.68 2.22 1.51 1.41 1.23 1.44 1.24 .91 .54 .58 .61
R .14 .14 .15 .14 .11 .17 .04 NS .04 NS .04 NS .07 NS .05 NS .10 NS
Note: All coefficients significant, p < .01, except where indicated.
a Marsden (1987) also analyzed differences in network size and kin composition by size of place, but this variable has not yet been coded for 2004 so comparable
analyses are not possible at this time. (The size of place variable is added to the data set after the data are collected, using the respondents’ addresses and current
Census tract information.)
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Table A6. Cooperativeness as Rated by the GSS Interviewer in 1985 and 2004
1985 (N = 1531) 1987 (N=1800) 2004 (N = 1467)
Level of Cooperativeness
Interested/Friendly 79.27% 77.05% 82.18%
Cooperative 16.74% 18.78% 15.13%
Restless/Impatient 3.58% 3.65% 2.65%
Hostile .41% .52% .04%
Note: X2 with 6 df = 32.07, p < 0.00
Table A7. Negative Binomial Regression of Size of Discussion Network
Model
Independent Variable I II III
Constant 1.069 1.129 .554
Wave2 (1 =1987) -.108 -.084 -.10
Wave3 (1=2004) -.349 -.336 -.404
Cooperative – -.187 -.121
Restless/Impatient – -.501 -.431
Hostile (Compared to Friendly/Interested) -.785 -.618
Number Missing in Preceding Questions -.197 -.162
Education (in yrs) .047
Female – .069
Agea– – -.002
Currently Married .014NS
Black – -.210
Other Race (Compared to White) -.177
Number of Adults in Household .018NS
Number of Children Ever Born .008NS
Number of Siblings .001NS
Alpha (Heterogeneity Coef.) 1.01*10-7 7.50*10-8 8.95*10-9
F 66.84 50.77 47.21
Note: All coefficients significant at p <.01, unless otherwise indicated.
a The squared term for age was not significant.
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Table A8. Logistic Regression Coefficients for Having Someone to Talk to About Important Matters (Dependent
Variable: No One to Talk to= 0, Someone to Talk to = 1)
Model
Independent Variable I II III IV
Constant 2.200 2.433 2.159 1.266
Wave2 (1 =1987) .851 .986 .897 1.273
Wave3 (1=2004) -1.080 -1.066 -1.392 -.247NS
Cooperative – -.364 -.159NS -.160NS
Restless/Impatient – -1.429 -1.293 -1.269
Hostile (Compared to Friendly/Interested) – -2.258 -1.968 -1.968
Number Missing in Previous Module -.549 -.473 -.469
Education (in yrs) .089 .152
Education*Wave2 – – – -.038NS
Education*Wave3 – – – -.100
Femaleb– – .254 .253
Agea– – -.018 -.016
Currently Married .315 .250NS
Black – -.903 -.863
Other Race -.284NS -.267NS
Number of Adults in Household .089NS
Number of Children Ever Born .018NS
Number of Siblings – – – -.013NS
F 118.56 50.78 37.28 27.12
Note: All coefficients significant at p < .01 unless otherwise indicated.
a The squared term for age was not significant.
b Significant at the p<.05 level.
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Journal of Democracy 6.1 (1995) 65-78 As featured on National Public Radio, The New York Times, and in other major media, we offer this sold-out, much-discussed Journal of Democracy article by Robert Putnam, "Bowling Alone." You can also find information at DemocracyNet about the Journal of Democracy and its sponsor, the National Endowment for Democracy. Many students of the new democracies that have emerged over the past decade and a half have emphasized the importance of a strong and active civil society to the consolidation of democracy. Especially with regard to the postcommunist countries, scholars and democratic activists alike have lamented the absence or obliteration of traditions of independent civic engagement and a widespread tendency toward passive reliance on the state. To those concerned with the weakness of civil societies in the developing or postcommunist world, the advanced Western democracies and above all the United States have typically been taken as models to be emulated. There is striking evidence, however, that the vibrancy of American civil society has notably declined over the past several decades. Ever since the publication of Alexis de Tocqueville's Democracy in America, the United States has played a central role in systematic studies of the links between democracy and civil society. Although this is in part because trends in American life are often regarded as harbingers of social modernization, it is also because America has traditionally been considered unusually "civic" (a reputation that, as we shall later see, has not been entirely unjustified). When Tocqueville visited the United States in the 1830s, it was the Americans' propensity for civic association that most impressed him as the key to their unprecedented ability to make democracy work. "Americans of all ages, all stations in life, and all types of disposition," he observed, "are forever forming associations. There are not only commercial and industrial associations in which all take part, but others of a thousand different types -- religious, moral, serious, futile, very general and very limited, immensely large and very minute. . . . Nothing, in my view, deserves more attention than the intellectual and moral associations in America." Recently, American social scientists of a neo-Tocquevillean bent have unearthed a wide range of empirical evidence that the quality of public life and the performance of social institutions (and not only in America) are indeed powerfully influenced by norms and networks of civic engagement. Researchers in such fields as education, urban poverty, unemployment, the control of crime and drug abuse, and even health have discovered that successful outcomes are more likely in civically engaged communities. Similarly, research on the varying economic attainments of different ethnic groups in the United States has demonstrated the importance of social bonds within each group. These results are consistent with research in a wide range of settings that demonstrates the vital importance of social networks for job placement and many other economic outcomes. Meanwhile, a seemingly unrelated body of research on the sociology of economic development has also focused attention on the role of social networks. Some of this work is situated in the developing countries, and some of it elucidates the peculiarly successful "network capitalism" of East Asia. Even in less exotic Western economies, however, researchers have discovered highly efficient, highly flexible "industrial districts" based on networks of collaboration among workers and small entrepreneurs. Far from being paleoindustrial anachronisms, these dense interpersonal and interorganizational networks undergird ultramodern industries, from the high tech of Silicon Valley to the high fashion of Benetton. The norms and networks of civic engagement also powerfully affect the performance of representative government. That, at least, was the central conclusion of my own 20-year, quasi-experimental study of subnational governments in different regions of Italy. Although all these regional governments seemed identical on paper, their levels of effectiveness varied dramatically. Systematic inquiry showed that the quality of governance was determined by longstanding traditions of civic engagement (or its absence). Voter turnout, newspaper readership, membership in choral societies and football clubs -- these were the hallmarks of a successful region. In fact, historical analysis suggested that these networks of organized reciprocity and civic solidarity...
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This article develops and tests an evolutionary model of the growth, decline, and demographic dynamics of voluntary organizations. The model demonstrates a strong analogy between the adaptive landscape of Sewall Wright (1931) and the exploitation surfaces generated by a model of member selection and retention for voluntary associations. The article connects the processes of membership recruitment and loss to the social networks connecting individuals. The model generates dynamic hypotheses about the time path of organizations in sociodemographic dimensions. A key idea in this model is that membership selection processes at the individual level produce adaptation in communities of organizations. The article concludes with an empirical example and some discussion of the implications of the model for a variety of research literatures.
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