Authors: Steve Richey, Alex Grider, and others

As early as 1945, social scientists have sought to identify the factors that affect satisfaction with one’s community. Early models, such as those employed by Davies (1945) used very simple models of community satisfaction based on measurements on topics such as the quality of liquor control, the presence of “persons of attainment” in the area, and the number of reputable families. More recent models have attempted to identify complex interactions among modern considerations including elements of one’s neighborhood including traffic flow, pollution, and walkability. (Ghorbanian, 2011) However, many studies fail to consider the impact of community satisfaction on overall life satisfaction, and vice versa. This study seeks to answer two questions. One, what factors of one’s community are important in determining overall community satisfaction? Two, how does community satisfaction impact overall life satisfaction?

In this study, the life satisfaction models proposed by Pavot & Diener (1993) and Blanchflower & Oswald (2011) were used as starting points for construction of a survey instrument. Elements of community satisfaction models as implemented by Ghorbanian (2011), Baker and Palmer (2006), Hipp (2009), and Goldberg (2003) were also incorporated. Progress on research that has attempted to tie community satisfaction to life satisfaction, primarily Auh & Cook (2009) and Swain & Hollar (2003) was used to tie together these two sides of the present research. Ultimately, a 76-item survey that accurately represented the variety of elements that contribute to community satisfaction was used.

In order to both test the validity of our model and to determine the practical implications of this model’s use for city planning, our team coordinated efforts with a town in the central United States with a population of approximately 50,000. This town features several large employers, two reputable universities, and a well-regarded public education district. By working with the government officials in the town, we not only gained the ability to test our model, but also provided the town with valuable planning information.

Several key terms will be used in this paper that relate to the topic at hand. First, we define satisfaction as “the evaluation of features of the physical and social environment”. (Ghorbanian, 2011) (Mesch & Manor, 1998) For this paper, we will refer to levels of satisfaction as high, meaning a generally favorable perception of a given factor or trait, or low, meaning a generally unfavorable perception of a given factor or trait. Also, in this paper the term community is intended to refer specifically to that area that a respondent considers to be their community. This term was intentionally left undefined on the survey instrument to allow respondents to use their own perception of what a community encompasses. The term community satisfaction then is intended to refer to an individual’s “evaluation of their place of residence” (Crowe, 2010).

Life satisfaction is a term that has received considerable interest in academic literature. While some of the first research into this field dates as far back as 1945, work by Andrew & Withey in 1976 was further underscored by the increasing importance placed, both in scientific literature and in American life, on quality of life and satisfaction instead of material goods. (Campbell, Converse & Rogers, 1976) Towards the turn of the millenia, complex modeling that accounted for a variety of factors determining community satisfaction was developed. (Sirgy, 2000) (Sirgy & Cornwell, 2001) Recent work in this field has begun to expand into developing objective measures of national well-being for use by the United Nations, and the emerging science of happiness economics. (Ott, 2010) (Potts, 2011)

Literature Review

Recent research has expanded preconceived notions of factors affecting community satisfaction in a variety of ways. The importance of social network structure on community sentiment (Crowe, 2010) and the role of volunteering and meeting with neighbors (Dassopoulos & Monnat, 2011) have been relatively recent additions to the body of literature on this topic. This has led to more advanced models of measurement, such as that used by Baker and Palmer (2006) to test a variety of hypotheses on significant contributors to quality of community life, that used by Yang (2008) to show that community development models will be perceived differently depending on community location and background, and that used by Hipp (2009) to test theories on the factors behind community satisfaction. Interestingly, research by Goldberg (2003) looked at seven independent variables in determining community satisfaction, for example: frequency of participation in recreation activities, amount of relatives in the community, and amount of adult friends in the community. Out of the seven variables studied, the top predictor of a resident’s satisfaction was the length of the residents’ stay in the community. This may appear as a variable that town officials would have little control over, but rather this points to the importance of maintaining a very low turnover rate for any town. Another point stressed by Goldberg to increase community satisfaction is to increase the number of service projects held in the community.

Per the existent literature, we will be investigating the effects of health factors (Phoenix & Mak, 2010), economic factors (Matarrita-Cascante, 2010), social factors (Michalos & Orlando, 2006), employment conditions (Near, Smith, Rice, & Hunt, 1984), and other significant determinants on community and neighborhood satisfaction (Ghorbanian, 2011) using a complete quality of life approach as advocated by Swain & Hollar (2003). Although national satisfaction can have an influence on life satisfaction (Morrison, Tay, & Diener, 2011), it was not selected for inclusion in this research.

Research Model

As mentioned, the intention of this research is to determine the relative impact of various factors on community satisfaction, and then to determine the relative impact of community satisfaction on life satisfaction as compared to other factors which are believed to be significant contributors to overall life satisfaction. The below table summarizes the variables which we have considered for this study. Community Satisfaction is both measured as an independent variable (IV) to determine its relative impact on life satisfaction, and as a dependent variable (DV) based on its component variables. All other variables were measured as a combination of response values to multiple questions, each on a five-point scale generally ranging from “Very Satisfied” to “Very Dissatisfied”.

Based on the existent literature, we expect each of the independent variables contributing to community satisfaction to have a positive relationship with community satisfaction, with the exception of cost of living, which should have an inverse relationship. Of the elements contributing to life satisfaction, we expect community satisfaction, health satisfaction, and economic satisfaction to the most significant contributors.

Methodology

For this study, an online survey was created with 76 questions designed to provide demographic and satisfaction data. With the assistance from the assistant city manager of a local town, we were provided with a list of the 15,376 unique addresses of town residents. From this list of addresses, 3,113 addresses were randomly selected using unrestricted probability sampling to receive a postcard invitation to participate in the survey. By using the link provided on the postcard, town residents could utilize an anonymous on-line survey application to provide their responses. Furthermore, the survey was available in the local library and city hall. Our expected response rate was approximately 10%, for a total of around 300 completed surveys. However, at the end of the survey we had received only 158 valid responses, for a response rate of approximately 5.1%. While this value is lower than expected, it is believed that the results of the survey will still be valid for analysis and town planning purposes.

Demographic Data

Chart: Respondent GenderDescription: Gender of respondents to survey of local town residents.Tags: Author: Steve Richeycharts powered by iCharts

Survey respondents were mostly female.

Chart: Ethnicity of Survey RespondentsDescription: Survey respondent ethnicity, as per the survey performed on local town residents.Tags: Author: Steve Richeycharts powered by iCharts

Respondent ethnicity was mostly Caucasian. This response is comparable to recent census data, which shows the county’s population to be 84.3% Caucasian. (US Census Bureau, 2010) Further research should place emphasis on representing other ethnic backgrounds.

Chart: Age of RespondentsDescription: Age of respondents to survey of local town residents.Tags: Author: Steve Richeycharts powered by iCharts

Initial concerns that this survey would over represent students in a town with two large colleges proved unfounded, as the majority of respondents were over the age of 45.

Chart: Employment Status of Survey RespondentsDescription: Job status of respondents to a survey of local town residents.Tags: Author: Steve Richeycharts powered by iCharts

Employment status provided some surprising figures; only 1.2% of respondents were unemployed, despite current economic concerns. Again, students show low levels of representation.

Chart: Respondent Marital StatusDescription: Marital status of respondents of survey of local town residents.Tags: Author: Steve Richeycharts powered by iCharts

Respondents were largely married, with just under three-quarters responding as such.

Chart: Number of Children at HomeDescription: The number of children respondents have at home, as part of a survey of local town residents.Tags: Author: Steve Richeycharts powered by iCharts

The low number of children at home, coupled with an average respondent age of approximately 48, may indicate that most respondents to this survey had already seen their children move out of the home.

Chart: Respondent Annual IncomeDescription: Annual income of survey respondents.Tags: Author: Steve Richeycharts powered by iCharts

Income levels show a fairly affluent respondent base, consistent with the general perception of the town. However, significant levels of low-income households are present, and there is no evidence to suggest that a particular income bracket is under-represented in the survey results.

Chart: Respondent Education LevelDescription: Education level of survey respondents.Tags: Author: Steve Richeycharts powered by iCharts

Education levels tend to be high, which makes sense given the number of colleges in the town, and the number of professors likely to be employed by those institutions.

Chart: Respondent Residence TimeDescription: Duration of local residence time, in years, from a survey of local town residents.Tags: Author: Steve Richeycharts powered by iCharts

With over a third of the survey respondents having lived in the town for over 21 years, we can expect those surveyed to be very familiar with community characteristics. However, this may introduce bias, which should be a consideration for future research.

Descriptive Statistics

The below table summarizes the mean and standard deviation values for variables impacting community satisfaction.

Across all variables, scores (on a five-point scale) were very high, indicating a high level of general satisfaction with community factors in this town. The highest scores were assigned to satisfaction with recreation facilities (x̅ = 4.30), satisfaction with availability of health care (x̅ = 4.29), and satisfaction with institutions of religious expression (x̅ = 4.23). The lowest scores were assigned to satisfaction with the local cost of living (x̅ = 3.48), satisfaction with diversity in the community (x̅ = 3.65) and satisfaction with local government bodies (x̅ = 3.80). Areas of common agreement included satisfaction with education and transit (s = 0.54), and contentious topics included satisfaction with local shopping (s = 0.81), as well as diversity and health care (s = 0.80).

The below table summarizes the mean and standard deviation values for variables impacting life satisfaction, as well as the life satisfaction variable itself.

Across all variables, scores were once again very high, indicating that survey respondents are experiencing a high level of general satisfaction with life. The highest scores were assigned to satisfaction with one’s family (x̅ = 4.27), satisfaction with one’s self (x̅ = 4.23), and satisfaction with romantic aspects of one’s life (x̅ = 4.16). The lowest scores were assigned to satisfaction with general economic conditions (x̅ = 3.82), satisfaction with one’s social pursuits (x̅ = 3.99), and one’s overall life satisfaction (x̅ = 4.00). Areas of common agreement included satisfaction with one’s life (s = 0.64) and satisfaction with one’s self (0.69), while differences arose in the topics of satisfaction with the economy (s = 1.04) and romantic satisfaction (s = 0.95).

Results & Discussion

While the descriptive statistics were likely to prove useful for town planning purposes, the intention of this study is to determine the relative importance of factors contributing to community satisfaction, and the relative importance of community satisfaction to life satisfaction. To this end, a variety of analysis methods were employed to determine direct and indirect correlations between variables.

A Pearson product-moment correlation coefficient was computed to assess the relationship between community satisfaction and each of the fifteen variables believed to impact community satisfaction. All results were found to be statistically significant (n=158, p<0.005) with one exception, which is noted below.

* The correlation between satisfaction with health care and community satisfaction was not found to be significant (p = 0.105)

As expected, there are positive correlations between each of these variables and community satisfaction, with the exception of health care. The strongest correlations exist for satisfaction with community safety (r = 0.508), satisfaction with community government (r = 0.469), and satisfaction with the community’s economy (r = 0.445). In other words, an increase in satisfaction with community safety, community government, or the community’s economy will result in significantly increased community satisfaction. While cost of living was expected to have a negative correlation with community satisfaction, that was not the case.

Similarly, a Pearson product-moment correlation coefficient was computed to assess the relationship between life satisfaction and each of the ten variables believed to impact life satisfaction. All results were found to be statistically significant (n = 158, p = 0.000).

As expected, there are also positive correlations between each of these variables and life satisfaction. The strongest correlations exist for satisfaction with one’s recreation activities (r = 0.481), satisfaction with one’s family (r = 0.476), and satisfaction with one’s self (r = 0.460). In other words, an increase in satisfaction with one’s recreation activities, one’s family, or one’s self will result in significantly increased life satisfaction.

Conclusion

Analysis of the results of our survey proved that we had identified variables that were significant contributors to community satisfaction and life satisfaction. Factors that have an especially strong correlation with community satisfaction include satisfaction with community safety, satisfaction with community government, and satisfaction with community economy. Factors that have an especially strong correlation with life satisfaction include satisfaction with one’s recreation activities, satisfaction with one’s family, and satisfaction with one’s self.

This research has shown many avenues for improvement in the future. First, demographic data showed that certain ethnicities, mainly Caucasians, were somewhat over-represented in this study, and future research should attempt to represent minorities more significantly. Second, demographic data also showed that respondents to this survey had lived in the participating town for quite some time (on average, at least 12.8 years). This may introduce a bias into the responses, and should be compensated for. Finally, the survey response rate was rather low at only 51% of the expected response rate. While it is believed that the results of this study are valid for model verification and town planning, future research should strive to improve the response rate so that results can be generalized to a broader area. Research has shown that a variety of methods could improve response rates, such as pre-notification, follow-up reminders, and even color choice. (Fox, Crask, and Kim, 1988) While web surveys do generally suffer from lower response rates, recent research has sought to alleviate this issue. (Fan & Yan, 2011)

In terms of practicality, these results do show a significant correlation between community satisfaction and life satisfaction (r=0.335, p<0.005). Given this, town planners can improve life satisfaction by focusing on those factors most important to community satisfaction, as described above. Furthermore, individuals looking to improve their general life satisfaction should attempt to improve their recreation activities, relationship with family, and feeling of satisfaction with their self.

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This study was originally submitted as a group project conducted for a class on Research Methodology.

(Header image credit Kristin Richey.)