Data Analysis

For each of the research questions below I discuss the analytic strategy, describe the variables, and discuss how each variable is operationalized. I use the full sample of both

partnered and single older adults because the impact of sex on health outcomes may be different depending on partnership status. In order to analyze the data, I use IBM SPSS Statistics 21.

Missing data has the potential to bias results as well as reducing the sample size which in turn reduces statistical power (Allison 2002). A common and simple way to deal with missing data is listwise or case wise deletion, but that can only be used if there the amount of missing data is very small and the missing data is missing at random (Allison 2002). Because this is a sensitive topic, data are unlikely to be missing at random. Because of this, I used multiple

imputation (Allison 2002). Finally, all procedures were reviewed and approved by the Institutional Review Board at Georgia State University.

3.2.1 Research Question One

Most research defines sex as PVI, even in research such as NSHAP, which defines sex however the respondent wants to define it, may still be limited by that definition if the

respondents defines sex as PVI. Measuring frequency of PVI and even frequency of self-defined sexual activity may promote a narrow idea of sexuality and aging. It is important to know the wide variety of sex acts of sex acts older adults are engaging in and their relationships with health.

I started by investigating the types of sexual acts that characterize the sexual activity of older adults and how type, and perceived quality vary across social groups. I conducted a univariate analysis and then bivariate analyses with chi-square tests and one-way ANOVAs depending on level of measurement. Finally, I use a multinomial logistic regression to

understand how different social groups are sorted into sexual activities based on the independent variables listed above. Multinomial logistic regression is used to assess the relationship between more than one non-ordered categorical outcome variable and several independent variables. The dependent variables are the different types of sex acts in which older adults engage. The

independent variables are demographic variables such as age, gender, race and ethnicity, and educational attainment, as well as functional status, health and health of one’s partner,

relationship status, relationship quality, interest in sex, and satisfaction with frequency of sex. See table 1 for a detailed list of variables and how they are operationalized in NSHAP.

I will first determine the most prevalent mutually exclusive combinations of sex acts for the multinomial regression. See sections 3.2.4 for specific information on how this variable was constructed empirically from the data on sex acts.

Hypotheses related to research question one are as follows:

H1: There will be demographic differences in sexual behaviors.

H2: Demographic variables, functional status, health and sexual health of one’s partner, relationship status, relationship quality, interest in sex, and satisfaction with frequency of sex will be predictive of engaging in certain types of sex acts.

3.2.2 Research Question Two

In order to investigate the relative impact of demographic, socioeconomic, relationship and sexual activity variables on the self-rated health of older adults, I estimated a series of ordered logit regression models. Ordered logit regression is used to estimate the impact of independent variables on ordered categorical dependent variables. Self-rated health, which has ordered response categories such as excellent, very good, good, fair, and poor, is the dependent variable for this research question. Independent variables are the frequency of different sex acts, education, age, gender, race and ethnicity. This will show the relative impact of the predominant types of sex acts on health before and after controlling for social demographic variables. See table 1 for a detailed list of variables and how they are operationalized in NSHAP.

H3: Engaging in sex acts will be predictive of greater self-rated health. The strength of these relationships will be different across sex act categories.

H4: When controlling for demographic, socioeconomic status and relationship variables, relationship between sex act categories and self-rated health will be diminished in

3.2.3 Research Question Three

In order to further specify the relationship between specific sex acts and health, I will estimate models that included relationship status, relationship quality, social support, interest in sex, satisfaction with amount of sex, and caregiving and assess whether these additional

variables account for or further specify the relationship between sexual activity and health. I estimate a series of ordered logit regressions with self-rated health as the dependent variable. Independent variables will be relationship status, relationship quality, social support, interest in sex, satisfaction with the frequency of sex, health of one’s partner, and caregiving. See table 1 for a detailed list of variables and how they are operationalized in NSHAP.

H5: Relationship status, relationship quality, social support, interest in sex, satisfaction with the frequency of sex, health of one’s partner, and caregiving will impact the strength or significance of the relationship between sex act categories and self-rated health.

H6: Those who have low levels of desire and interest and choose not to maintain sexual activity will have higher self-rated health than those who have low levels of desire and interest and maintain sexual activity.