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Key Studies on Relationships and Happiness

To read a comprehensive review of each key study, simply click on each study’s citation:

Brown, S. L., Nesse, R. M., Vinokur, A. D., & Smith, D. M. (2003). Providing Social Support May Be More Beneficial Than Receiving It: Results From a Prospective Study of Mortality. Psychological Science, 14(4), 320–327.

Previous research concerning gerontology and social support has demonstrated a strong association between social contact (social support) and health and well-being. However, it still remains unclear whether receiving social support can account for the same benefits in health and wellness. For example, a study by Lu and Argyle (1992) demonstrated that individuals may feel guilty and anxiety when depending on other individuals for support. In addition, “feeling like a burden” to others who provide support is correlated with increased suicidal tendencies (Brown, Dahlen, Mills, Rick, and Biblarz, 1999). Because social contact is often related to supporting others (or being supported), the advantages of social contact may actually instead be due to the benefits associated with providing support specifically.

In this investigation, Brown et al (2003) utilized data from the Changing Lives of Older Couples (CLOC) sample to address two key questions: (1) Do the advantages of providing social support account for the benefits of social contact, which are interpreted often as resulting from support received from others? and (2) Once the provision of support and dependence are both controlled, does receiving support have an impact on mortality?

Brown’s study from 2003 was identified as a key study because it had an emphasis on providing social support rather than receiving it. It was discovered that providing social support could decrease mortality.

Participants were recruited from the CLOC study, which is a prospective study of 1,532 individuals from Detroit and the surrounding area. The sample of respondents for this study consisted of 846 adults, who were married. Face-to-face interviews were administered initially as a baseline assessment, and additional measures were administered over 11 months between 1987 and 1988. Out of the sample, 134 individuals died over the five years of the study.
Mortality was continually monitored over a five-year period, and various baseline measures were also assessed, including giving instrumental support to others (GISO), providing and receiving emotional support (GESS), and a variety of control variables consisting of demographic, health, and individual difference data. Other aspects of the marital relationship were also assessed, such as equity and marital satisfaction.

First, to investigate whether providing instrumental support decreased the risk of mortality, a hierarchical logistic regression procedure was conducted. The results demonstrated that social contact reduced the risk of mortality (p < 0.05); this finding was also echoed in the literature. The risk of mortality was further decreased by GISO (p < 0.001), but was only slightly increased by RISO (p < 0.10).

Brown et al (2003) note that no single mediator of the relationship between giving support and mortality was identified; however, other studies demonstrate that aiding others increases positive emotion and will increase cardiovascular recovery, thus implicitly promoting health.

This study, which utilizes a longitudinal and prospective design, demonstrates that mortality was significantly reduced for individuals who provided support to friends, relatives, peers, and neighbors. However, receiving support had no further effect on mortality after the giving of support was taken under consideration. This research has strong implications on the appropriate design of clinical interventions that may currently aim to help individuals feel more supported.

Noted Limitations/Future Directions
As this study demonstrates, further research must deeply examine the assumption that social support in general reduces mortality. In addition, studies may seek to redesign interventions that emphasize receiving social support after more evidence is gathered.

Burgoyne, R., & Renwick, R. (2004). Social support and quality of life over time among adults living with HIV in the HAART era. Social Science & Medicine, 58(7), 1353–1366.

The present longitudinal study considers stability in perceived social support and the relationship between social support and health-related quality of life in a sample of 41 adult outpatients living with HIV/AIDS (PHA) in Canada. Previous research has determined associations between perceived social support and adjustment to HIV diagnosis and its potentially chronic, disabling course. This study marks a shift away from an emphasis on the psychoneuroimmunological effects of stress toward interest in the potential protective effect of social support; however, such causality has yet to be determined. Current research strives to find causal directionality of relations between social support and overall health and quality of life for PHA.

The purpose of this study is to systematically assess social support stability as well as cross-sectional associations between social support and quality of life for a sample of HIV-positive adults over an extended period of outpatient clinical care in the context of HAART. Social support and quality of life ratings obtained at the time of initial outpatient clinic registration were compared to ratings available at 2- and 4-year follow-ups. The study also examines the predictive potential and causal directionality between social support and quality of life and changes in these factors over time.

Burgoyne’s study from 2004 was identified as a key study because it examined perceived social support in participants with HIV/AIDS.

Participants (N= 41) were PHA outpatients attending a tertiary-care HIV/AIDS ambulatory clinic within a Toronto, Canada teaching hospital in 1997. The sample was 85% male and 15% female (Mage= 38.8 years; range 22-61 years), and disease stages were 29% asymptomatic stage, 32% symptomatic stage, and 39% AIDS stage. After initial orientation to the clinic, participants were asked to complete and information form and standardized measures of social support and quality of life. In 1999, 2 years (+/- 1 month) after initial measures, participants were approached to complete follow-up survey measures. Data on medical and immune function factors were collected as well. A third wave was conducted 4 years after initial data collection. Biological measures of clinical symptoms were also conducted at the three time points, as symptom burden has been shown to influence quality of life.

Perceived social support was measured using three dimensions of the Medical Outcomes Survey (MOS) Social Support Survey (SSS). The dimensions represent types of support available “if needed” from unspecified sources: Affectionate support (expressions of love and affection), Positive Social Interaction support (availability of others with whom to share enjoyable time), and Emotional-Informational support (understanding, encouragement, guidance, and information). Health-related quality of life was measured by the MOS Short-Form-36 (Ware, Snow, Kosiniski, & Gandek, 1993). The questionnaire consists of 36 items representing 8 dimensions of quality of life: physical functioning, body pain, role limitations due to physical problems, general health perception, vitality/energy, social functioning, mental health, and role limitations due to emotional problems.

SSS and SF-36 data from the 3 waves were analyzed using a one-way repeated measures ANOVA with corresponding post hoc one-sample paired t-tests. Because the distribution of data showed some degree of skew, non-parametric tests were used to check for consistency of findings in terms of longitudinal changes; effectively, Friedman tests were also used for omnibus evaluation across the three time points. Associations between SSS and SF-36 ratings cross-sectionally as well as SSS changes and SF-36 changes were determined using regression analyses. Thus, a multivariate regression model was employed to determine cross-sectionally the relative effects of clinical status (symptoms, CD4) and SSS ratings on physical and mental ratings at the three study points. Predictive potential between SSS and SF-36 ratings was examined through a set of cross-lagged analyses (Figure 1). This method is based on the premise that causal directionality between two variables can be theoretically verified by comparison of relative strength of association between each variable at one point with the other variable at a later point in time.

Analysis of clinical symptoms showed peak immunologic/virologic outcomes at some point close to T2. The increase in mean number of symptoms from T1 to T3 was significant despite a decrease in number of symptoms from T2 to T3. These results suggest an overall increase in symptom burden that is attenuated over time.
Results of SSS ratings show a slight reduction over time that is not statistically significant. However, a within-group change in the Affection subscale (Friedman test, p< 0.04) reflected a statistically significant T1–T2 reduction (Wilcoxon signed ranks test, p< 0.05) in that particular dimension of social support. Cross-lagged analyses revealed statistically non-significant associations between ratings of SSS subscales and subsequent ratings of SF-36 component summaries, and vice versa. Illumination of the direction of causation between social support perceptions and health-related quality of life perceptions was largely unsuccessful.

The results of this longitudinal study suggested slight 4-year reductions in social support perceptions and stability in quality of life ratings. Cross-sectional relationships between perceptions of available social support and ratings of quality of life were inconsistent for this sample. The strongest associations were seen at the first follow-up mark, while initial and final time points showed minimal relation.

Relative importance of support at different phases of illness and treatment could explain the predominance if T2 associations. The association between social support and quality of life may have evolved due to increased health-information needs and communication with health providers. Stronger positive associations between social support and quality of life at T2, when the mean number of symptoms reported was 71% greater compared to T1 and 28% greater compared to T3, suggest that perceptions of social support had the potential to mitigate the negative consequences of symptoms and/or side effects on quality of life perceptions at that time. Overriding clinical factors could explain the lack of evidence supporting strong relationships between changes in functional social support ratings and changes in quality of life.

Overall, findings support the need for future study of social support as a potential buffer against the negative health and quality of life outcomes of HIV infection.

Diener, E., & Seligman, M. (2002). Very Happy People. Psychological Science, 13(1).

There is a vast amount of literature focusing on very unhappy individuals, but not a lot on people who are very happy. In this study, researchers investigate factors that would theoretically influence high happiness: social relationships, personality, psychopathology, and variables (religiosity, exercise, etc.) For a variable to be considered for happiness, all of the “happy” individuals should possess this variable. One of the questions they have is, is there a key variable that makes others happy? The researchers also want to test and see if happier individuals experience euphoric feelings or if they just felt moderately pleasant thoughts. Diener’s study from 2002 was identified as a key study because it separated college students into three different happiness groups (high, norm, low) and discovered that the highest happiness group had more satisfying social relations

The participants of this study consisted of 222 college students that agreed to be involved for an entire semester. The participants took questionnaires (Satisfaction With Life Scale, Global self-reported affect balance, Informant affect balance, Daily affect balance) and were then separated into three groups (very happy, average, and least happy). The participants had to fill out daily reports for 51 days. The highest and lowest 10% were set aside and the remaining participants took three more questionnaires (Memory event recall balance, Trait self-description, Interview suicide measure) to refine the assignments of highest, middle, and lowest. The researchers then compared the upper 10% (n=22), average (n=60) and lowest 10% (n=24).

The very happy people scored about a 30 on the life satisfaction scale, 35 being the highest. The had never thought of suicide, could recall more good events than bad, and experienced more positive than negative emotions on a daily basis. The unhappy group experienced and equal amount of positive and negative feelings on a daily basis, and were only somewhat satisfied with their lives. The very happy group spent the least time alone and was rated highest on good relationships. The unhappy group rated themselves with subpar relations with family and friends. According to the MMPHI both groups scored in the normal range but the very happy people were more extraverted, lower neuroticism, and higher agreeableness. The very happy people were not ecstatic and a few extra variables but nothing significant enough.

Very happy people have rich and satisfying social relationships. Unhappy people have social relationships that are below average. The researchers compared social relationships to food, that it is universally important to the human mood. That means that there is no guarantee that social relationships cause happiness.

Noted Limitations/Future Directions
A big limitation is that they had a small sample size making it difficult to compare to real world situations. Due to the nature of the cross sectional data the researchers are not sure if that rich social lives cause happy people or if happy people cause rich social lives.

Helsen, M., Vollebergh, W., & Meeus, W. (2000). Social support from parents and friends and emotional problems in adolescence. Journal of Youth and Adolescence, 29(3), 319–335.

It has been proven that a quarter of Dutch adolescents have experienced serious emotional problems (depression, loneliness, low self-esteem, social isolation, and suicidal thoughts). As a child your main source of social support comes from your parents while as you age it becomes more centered around your peers. Research has shown that support from parents provides a better indicator of positive development rather than peer support. There are also studies that show relationships with parents have a positive correlation to relationship with peers. The current researchers focused on the shift in social support at adolescents and their effects on psychological well-being.

Helsen’s study from 2000 was identified as a key study because it examined two types of relationships (friends and family) and at what age does the support switch importance from one to the other.

This study used data from the Utrecht Study of Adolescent Development from 1991. The participants consisted of 2589 young individuals in four different age categories: early adolescents (12-14, n = 549), middle adolescents (15-17, n = 798), late adolescents (18-20, n = 645), and post adolescence (21-24, n = 597). The USAD recived data from both questionnaires and interviews but the present researchers only focused on the questionnaire data. The questionnaires examined parental and friends’ support and emotional problems (HGQ, the Centril ladder, Mini-VOEG, and suicidal thoughts).

There was no difference between perceived parental support (X =67:2 and 66.1 respectively). Girls did experience more support from friends (X = 66.7) than boys (X = 56.7). There was a 10.7% of variance in emotional problems where girls reported having more than men. Parental support was negatively correlated with emotional issues regardless of the support received from friends.

Around the age of 16 there is a shift of support. Perceived social support from parents decreases while from friends it increases. Parental support was strongly related to emotional problems in adolescents. There was only a significance to well-being from friends when perceived parental support was apparent.

Noted Limitations and Future Directions
Their population was only limited to a small sample and with a limited range of emotional issues.

Krause, N. (2002) Church-Based Social Support and Health in Old-Age: Exploring Variations in Race. J Gerontol B Psychol Sci Soc Sci 57 (6): S332 – S347.

In the present study, Krause evaluates a proposed conceptual model exploring the relationship between church-based support and health. Literature suggests that religious involvement is associated with good health throughout adulthood (e.g. Koenig, McCullough, & Larson, 2001), and recent studies continue to examine how such protective factors arise from religion. However, one particular domain lacks substantial investigation: the effects of social relationships within the church. The first goal of this study is to assess the relationship between religious support and health in late life, with specific focus on beneficial health effects. Second, the study will explore race as a moderating factor in the relationship between social support and health. More specifically, the church-related social ties of older African Americans are hypothesized to be stronger than those of older White persons.

Krause’s study from 2002 was identified as a key study because it observed older adults with religious beliefs and how religion and the community social support play a role in well-being.

Data was extracted from a nationwide survey of 1,126 elderly adults (66 years of age and older). All participants were of the Christian faith, and extensive sampling techniques yielded 748 older White people and 752 older Black people. Participants then completed self-report measures of frequency of church attendance, congregation cohesiveness, spiritual support, emotional support from church members, connectedness with God, optimism, self-rated health, race, and demographic control measures. Participants rated their response to each measure on a 4-item Likert scale.
Analyses first sought to test for differential involvement in religion between older Black and White people. Further analyses assessed the differential impact of religion across the two groups. Each racial group was pooled prior to the use of Version 8.50 of the LISREL statistical software program (du Toit & du Toit, 2001).
Substantive findings were consistent with differential hypotheses, revealing that older Black people are more deeply invested in their religion than older White people. When compared to older Black people, older White people reported less cohesive congregations (ß = -.103; p < .001). Older White adults also indicated that they receive less spiritual support (ß = -.223; p < .001) and less emotional support from church members (ß = -.201; p < .001). Additionally, data suggest that, when compared to elderly Black adults, older White adults feel less connected to God (ß = -.117; p < .001). Racial differences extended beyond religion items, as older White adults indicated lower optimism than older Black adults (ß = -.173; p < .001). Finally, older White Americans did rate subjective health slightly higher than older Black people (ß = -.103; p < .001).

Results of the present study provide evidence against the association between religion and health. Data suggest that health-related benefits of church-based support may be primarily associated with the spiritual support provided by other members. Interestingly, results show that older Black adults may derive greater health-related benefits from religion simply because of increased involvement. Although Krause’s work is noteworthy in its examination of older adult social support, one major limitation is the use of self-reported health measures. Future study might consider alternative measures of physical health, as well as examination of type and intimacy of church social support in both groups.

Loscocco, K., & Spitze, G. (1990). Working Conditions, Social Support, and the Well-Being of Female and Male Factory Workers. Journal of Health and Social Behavior, 31, 313–327.


Work is a major characteristic of life, and has the propensity of influencing many different facets of an individual’s personality. Because of the challenges facing workers around the world, distress has now become a hazard that many must face when participating in the working world. Because occupations requiring an intense amount of physical labor have decreased in number over the years, many individuals are instead growing increasingly concerned with the psychosocial facets of work – in particular, the link between work (or occupational characteristics) and well-being. The relationship between work and well-being is one that can in turn influence productivity or disease susceptibility (Loscocco and Spitze, 1990).

Although previous research has examined the influence of particular occupational conditions on health outcomes, few research studies have included women or gender into their models that describe the mechanisms by which working conditions have an impact upon well-being. Thus, Loscocco and Spitze (1990) note that when compared to full-time female homemakers, the health of employed women compares favorably (p. 313). The paucity of studies concerning gender or women in the relationship between work and well-being may be due to the difficulty of obtaining appropriate samples due to high levels of gender segregation in industrialized countries.

In the literature, job characteristics have often been categorized into three main groups: (1) job demands, (2) job deprivations and rewards, and (3) the nature of the work environment. “Job demands” is the most widely studied category, and encompasses an understanding of role conflict, role overload, and the ways in which people feel able to effectively perform occupational responsibilities. Previous research has demonstrated that ambiguous role demands, an excessive workload, or forced overtime reduce mental well-being (Caplan et al. 1980). In terms of job deprivation and rewards, the research demonstrates that the lack of intrinsic rewards reduces emotional well-being (Caplan et al. 1980). Work environment also plays a role in influencing well-being, as those who report working in a supportive atmosphere demonstrate better mental health than employees who do not work in a similar atmosphere (Caplan et al 1980).

The objective of the current investigation was to build upon past research concerning the influence of working conditions and social support on the well-being of men and women. Loscocco and Spitze (1990) utilize a sample of blue-collar workers (both men and women) in many plants of manufacturing industries,, and they measure working conditions through organizational sources and self-reports. Furthermore, they also examine the buffering effects of workplace social support in addition to examining the direct effects that social support at work may provide.

Losccocco’s study from 1990 was identified as a key study because it analyzed factory working conditions for both male and female employees. It was determined that social support has a direct effect on well-being

The data were derived from a 1982 survey of manufacturing organizations and their employees (Lincoln and Kalleberg, 1985). The data was provided from plants in seven manufacturing industries of Indiana: electrical machinery, chemicals, prefabricated metals, food processing, nonelectrical machinery, transportation equipment, and printing/publishing. Plants were sampled randomly. The response rate of firms that decided to participate in the study was 42%.

To gather information regarding organizational structure and context, interviews took place with plant and personnel managers, and information was collected from company documents. Top managers administered questionnaires to full-time employees at each worksite. The employee response rate was 65%, and the employees provided data on their work context and on specific jobs. The analyses in this study are limited to 2260 nonsupervisory employees, who perform either skilled, semiskilled, or unskilled blue collar jobs in 27 different plants. Women composed 29% of the subsample.

To measure well-being, two dependent variables were utilized: distress and happiness. Distress was measured by asking about physiological symptoms and the frequency for the symptoms. Happiness was measured by a single-item question asking about how happy the participant was in current days (thus providing a measure of emotional/psychological state).

To measure job demands, two variables were utilized: (1) the number of hours overtime the participant worked on average per month, and (2) two questions regarding job strain (the extent to which the worker feels overloaded/overwhelmed). Job deprivations and rewards were measured by asking about job characteristics. Four questions also asked about substantive complexity (how long it would take the worker to train someone to do his job). Autonomy, income, span of control of supervisors, work environment, work social support, co-worker satisfaction, and supervisor support were also all assessed.

To calculate the quantitative results, a statistical analysis was initially conducted by estimating the impact of personal characteristics, work conditions, and work-related social support upon the emotional well-being of both female and male samples of blue-collar workers. Equations were presented separately for men and women.

Then, Loscocco and Spitze (1990) assess the role of workplace social support in either buffering or conditioning the impact of job demands upon well-being. This is done by regression equations, in which a product term reflects the interaction between a job condition and a measure of social support. Further equations were generated to examine the extent to which social support (from co-workers, supervisors, or company programs) serves as a buffer for the impact of each job condition on mental health.


The effects of working conditions upon distress did not show significant differences among the two genders. Job strain was found to increase distress, although working overtime does not increase distress, perhaps because working overtime may be a choice that individuals make at times. The findings also demonstrated increased distress if employees worked in a physical environment without strong communication; reduced distress was demonstrated when employees had satisfying relationships with their co-workers. In terms of personal characteristics, children were found to increase the distress experienced by women, which is echoed by previous research (McLanahan and Adams, 1987).

Job strain was the only job characteristic found to affect men and women differently. Specifically, job strain had a stronger negative impact upon women’s happiness levels than on men’s. Loscocco and Spitze (1990) write that the finding indicates that happiness among women is just as responsive (if not more responsive) to job conditions as is happiness among men. Overtime did not influence happiness levels. Substantive complexity and span of control were correlated with happiness; specifically, individuals with more complex jobs and less close supervision were happier. Social support also exerted a direct effect on well-being among both men and women. Education had a negative effect on women’s happiness, but had no effect on the happiness of men.

Buffering Effects
The evidence demonstrates very little support for the hypothesized buffering effect of social support upon the relationship between work conditions and distress or happiness. Social support variables were demonstrated to exert direct effects upon well-being, rather than buffering effects.

In this research study, Loscocco and Spitze (1990) investigated the mechanisms by which individuals are influenced by the work that they perform – particularly the ways in which job demands, job rewards, and social support influence well-being (distress and happiness) among both men and women working as blue-collar employees in Midwestern industries. After analyzing cross-sectional data, their results demonstrate that social support in the workplace exerts a direct effect (rather than a buffering effect) on well-being. There were no significant gender differences in the effects of working conditions upon distress or happiness.

Noted Limitations and Future Directions:
In this research study, Loscocco and Spitze (1990) investigated the mechanisms by which individuals are influenced by the work that they perform – particularly the ways in which job demands, job rewards, and social support influence well-being (distress and happiness) among both men and women working as blue-collar employees in Midwestern industries. After analyzing cross-sectional data, their results demonstrate that social support in the workplace exerts a direct effect (rather than a buffering effect) on well-being. There are no significant gender differences in the effects of working conditions upon distress or happiness.

Rüesch, P., Graf, J., Meyer, P. C., Rössler, W., & Hell, D. (2004). Occupation, social support and quality of life in persons with schizophrenic or affective disorders. Social Psychiatry and Psychiatric Epidemiology, 39(9), 686–694.


Work is a key facet of human life and is responsible for providing a social position, identity, and income. Work is positively associated with mental health and has been demonstrated in previous studies to improve psychological well-being among individuals who are mentally ill (Cook 2000). Despite the consensus of work’s positive relation to mental health, the World Health Organization estimates that 90% of individuals with a serious psychiatric background are unemployed (Harnois, 2000). Individuals who are mentally ill often indicate that they are dissatisfied about being unemployed. In a study of psychiatric inpatients, most chronic schizophrenia patients reported that a good quality of life would be constituted by social relationships and employment (Angermeyer, Holzinger, and Matschinger, 1999).

Unemployment does not solely affect income and independence, but also one’s quality of life, which can be considered subjectively (one’s own physical, psychological, and social well-being) or objectively (one’s broader conditions of living) (Ruesch et al. 2004). The consensus in psychological literature is that paid employment is associated with better subjective quality of life, improved social functioning, and a reduction in psychiatric symptoms (qtd. in Ruesch et al, 2004, p. 687).

The objective of this investigation is to examine the relationship between work status/work objective and subjective quality of life among individuals with severe mental illness (SMI). This objective is achieved by addressing three main research questions: (1) In what type of work (competitive employment, minimal work, sheltered work, etc) are people with severe mental illness engaged? (2) Upon controlling for mental illness, is an individual’s occupation related to subjective quality of life? (3) To what extent is the relationship between occupation and subjective quality of life mediated by objective quality of life (in terms of economic conditions and social network)?

Ruesch’s study from 2004 was identified as a key study because it examined the relationship between work and life satisfaction and it was determined that social support played a large role.

The present study was executed between January 2000 and March 2001 in the inpatient wards of two Swiss mental hospitals in Zurich. To participate in the study, the inclusion criteria consisted of: being aged between 20 and 50 years, having a main diagnosis of schizophrenic or delusional disorder (or affective disorder), and knowing the German language sufficiently. After written, informed consent had been gathered, the sample was composed of 261 inpatients (102 women and 159 men). The interview sample was compared to the population of all hospital admissions. In terms of psychiatric diagnosis, 158 inpatients suffered from schizophrenia, schizotypal, or delusional disorders, and 103 subjects had a diagnosis of an affective disorder.

To measure subjective quality of life, the German version of the WHOQOL instrument was used that examined four domains: physical health, psychological well-being, social relationships, and the environment. LUNST scales were used as a measure of social support, and the scales were adapted from the Social Support Questionnaire (Schaefer, et al.). To measure objective quality of life, researchers assessed housing, main source of living, and income. The occupational situation of participants was also assessed based on questions regarding: competitive employment, employment in a sheltered workplace, occupation in a hospital rehabilitation unit, educational activities, unpaid work, or no occupation similar to that mentioned above.

In terms of statistical analysis, chi-square tests and ANCOVA were utilized for exploratory analysis, and the four quality of life scales were significantly intercorrelated. Structural Equation Modelling (SEM) with latent variables was used to perform further statistical analysis.

Types of occupations
Approximately half of the participants had worked in paid employment on the competitive labor market as a main occupation for the previous year. Thirteen percent of subjects (n=31) had been employed in a “sheltered” workplace. Sixteen percent (n=43) had been involved in unpaid activities similar to “work,” such as education, caring for children, housework, etc. Twenty-five percent of subjects had no occupation.

Illness-related variables
In this study, many different illness-related variables were assessed, including age of onset of the disorder, age at first psychiatric hospitalization, number of psychiatric admissions, days hospitalized in the previous year, psychopathological symptoms, or diagnosis. One-way ANOVAs were conducted, using the type of occupation as the independent variable and the illness characteristics as the dependent variable. The findings demonstrate that the type of occupation is significantly correlated with treatment variables (such as number of psychiatric admissions during lifetime). Specifically: competitive employment (< sheltered work) and number of admissions last year (F = 3.2; p = 0.025); competitive employment (< no occupation) and days hospitalized last year (F = 9.8, p < 0.001); competitive employment and unpaid work (< no occupation). Diagnosis was also found to be related to occupation (X2 =14.1; p = 0.003). Inpatients diagnosed with schizophrenia or delusional disorder were over-represented in “sheltered” workplaces. In contrast, a greater number of inpatients diagnosed with an affective disorder were found to be engaged in unpaid work.

Objective quality of life
The type of occupation is significantly related to objective quality of life variables, including housing, living, income, and social network. Most participants (75%) that had competitive employment reported that they supported themselves on their own income; other subjects were supported by welfare or by a partner. In terms of social network, except for partner relationships, the frequency of relationships is linked to occupational status. Thus, subjects with competitive employment have the greatest number of frequent, regular contacts with other individuals (friends, coworkers, relatives, etc).

Subjective quality of life
In terms of the subjective quality of life, occupation is linked to three out of the four specific “domains” of subjective quality of life: physical well-being, social relationships, and environment. This relationship remains constant even upon controlling for illness variables (ANCOVA). In terms of the domains of social relationships and environment, participants with competitive employment, as well as individuals in unpaid work, were found to have stronger reports of subjective quality of life when compared to individuals without any occupation. Subjects with unpaid work are more satisfied with their physical well-being than those without an occupation.

Structural equation modelling
Structural equation modeling suggests that for psychiatric inpatients, having no work-like occupation is substantially (negatively) related to the “Living” domain as well as “Social Support.” Social support positively influences subjective quality of life, satisfaction with health, and satisfaction with social ties. The model also indicates that occupation is significantly linked to only one latent variable of subjective quality of life, which is social ties. This demonstrates that occupation exerts an indirect effect upon inpatients’ Satisfaction with Social Ties.

The work of Ruesch et al (2004) demonstrates that having a work-like occupation is important for the quality of life experienced by the psychiatric inpatients in this study. The relationship between work occupation and quality of life is particularly true in terms of social network, social support, and life satisfaction.

Noted Limitations and Future Directions
The authors note that one key limitation in this study is that it utilizes a cross-sectional and naturalistic design, so the correlations between occupation and social network/social support should be only viewed as “causal” with limitations. In addition, some participants in this study reported breaks in their working life during the working period that was assessed (the previous year). The work history, rather than the main occupation, could have influenced quality of life.

When creating employment opportunities for individuals who are psychiatrically ill, further studies may seek to build upon this work by taking into account the social support that is provided at the workplace Specifically, for mentally ill individuals who are taking part in “sheltered work” opportunities, the payment scheme ought to be improved.

Smith, D. A., Breiding, M. J., & Papp, L. M. (2012). Depressive moods and marital happiness: Within-person synchrony, moderators, and meaning. Journal of Family Psychology, 26(3), 338–347.

Marital issues can play a huge role in depression. A study done by the Epidemiological Catchment Area reported that out of their sample 45.5% of maritally distress women had depression. A study done by O’Leary et al. found that newlyweds depression increased 10-fold. There are several moderators that can contribute to marital distress and depression. There are three main one the researchers focused on: sociotropy vs autonomy, excessive reassurance-seeking, avoidant and anxious attachment. A study done by Beck examined sociotropic people and autonomous people. He found that sociotropic people were susceptible to depression while autonomous people were less susceptible feeling more independent of their relationship. Individuals who tend to need excessive reassurance are more often to be depressed than people who do not need that reassurance.Insecure attachment (avoidant and anxious) style have been proven to be associated with marital distress. There has not been much research on maritally distressed couples which is what these researchers wanted to pursue.

Smith’s study from 2012 was identified as a key study because it examined how marital distress affects depression. The higher the marital distress the higher the depression

This study consisted of 55 married women, 24 newlyweds and 31 maritally. Both groups of participants were asked to complete a demographic questionnaire, individual difference measures as well as daily dairies every evening before bed. The measures examined were sociotropy and autonomy (PSI-II), reassurance seeking (DIRI), avoidant and anxious attachment, marital adjustment (DAS), daily depressive mood, (PANAS), and marital happiness (MHS).

This study consisted of 55 married women, 24 newlyweds and 31 maritally. Both groups of participants were asked to complete a demographic questionnaire, individual difference measures as well as daily dairies every evening before bed. The measures examined were sociotropy and autonomy (PSI-II), reassurance seeking (DIRI), avoidant and anxious attachment, marital adjustment (DAS), daily depressive mood, (PANAS), and marital happiness (MHS).

Greater marital distress was associated with greater depressive symptoms. Marital and depressive symptoms correlate at the individual difference as well as the within-person effects. The distressed group had more depressive symptoms, were married longer, less maritally satisfied, and had children. This proves that relationship length is a moderator to marital happiness.

Noted Limitations and Future Directions:
A weakness the researchers noted was that only wives were studied for this project. They also did not have any behavioral assessments and all the associations were congruent. The researchers suggest future longitudinal studies on marriage and depression to examine more associations between them, long term.

Stillman, T. F., Baumeister, R. F., Lambert, N. M., Crescioni, A. W., DeWall, C. N., & Fincham, F. D. (2009). Alone and without purpose: Life loses meaning following social exclusion. Journal of Experimental Social Psychology, 45(4), 686–694.

Where people find meaning in life has been investigated with continuous studies for years. Because meaning is based on an array of multifactorial elements, it is difficult to pinpoint a specific entity; furthermore, because of this complexity, life’s meaning is not solely dependent on social relationships. However, social interactions have played a large part in the evolutionary mechanics of the human population. Therefore, this study investigated the idea that social exclusion is the cause of a global decrease in the self-declaration of having a meaningful life.

Stillman’s study from 2009 was identified as a key study because it examined how being socially rejected and ignored would affect happiness and perception of meaningfulness of life.

This experiment was a four part study.

Study 1 – The first study consisted of 108 undergraduates where 75% were female. Researchers told the participants that they would be coupled with another participant of the same gender and that they would get to meet after they made a video about their career aspirations. In the laboratory the participants would watch a video by they partner/confederate and would make a response video. The experimenter left the room to supposedly give the video to the partner. The experimenter would return with one of three answers classifying the participants into a group. They were either rejected, their partner had to leave (control), or their partner accepted their video. All the participants were given a questionnaire (Daily meaning Scale, DMS) after receiving this information.

Study 2 – The second study was a recreation of the first study but instead of using videos for social acceptance, the participants would play an electronic tossing game with the confederate.

Study 3 (Conceptualized) – The third study consisted of 121 undergraduate participants. This study is similar to the second. The participants were told they would be playing Cyberball with three other participants. In fact there were no other participants. Cyberball simulates other players for social exclusion testing. The participants were split up into three groups:
o Control – The ball was tossed to everyone at an even frequency
o Ostracism – Participants were thrown the ball at first then ignored
o High-Inclusion – Received 22% more throws than the control.

Study 3 (actual) – This study consisted of 202 undergraduate participants. The students were asked to complete a questionnaire at their leisure. The dependent variable was meaningfulness (The Meaning in Life Questionnaire – MLQ and the DMS). The independent variables tested were social exclusion (UCLA Loneliness Scale), depression (Center of Epidemiologic Studies Depression Scale), happiness (Subjective Happiness Scale), optimism (Life Orientation Test-R), and mood (BMIS)

Study 4 – The fourth study was similar to the third. There were 212 undergraduate participants. The students completed a questionnaire with the dependent variable meaningfulness (MLQ) and the independent variable social exclusion (UCLA Loneliness Scale). The researchers measured purpose, value, efficacy, and self-worth.

Study 1 & 2- Planned comparisons showed that rejected participants rated life as less meaningful (M=6.26; SD=.71) than did accepted participants (M= 6.54; SD=.57), F(1,105)=4.32, p=.04; d=.43. Likewise, meaning scores were lower for rejected participants than control participants (M=6.62; SD=.46), F(1,105)=5.65; p=.02; d =.60.

Study 3 (Conceptualized) – Planned comparisons confirmed that life was rated as more meaningless by ostracized participants than by those in the control condition, F(1,115)=3.83, p=.05; d=.33, and more than by those in the high-inclusion condition, F(1,115)=7.79, p=.01; d=.39.

Study 3 – Loneliness predicted the presence of meaning, r =−.35; p<.001, such that more loneliness was associated with less meaning. Other variables were related to meaningfulness which were: depression (r =−.24, p=.001), happiness (r =.36, p<.001), optimism (r =.21, p=.003), and mood valence (r =.30, p <.001).

Study 4 – Higher levels of loneliness were positively correlated to lower levels of meaning.

Social exclusion caused a reduced in the perception of life as meaningful. With all the studies it was shown that loneliness and rejection were associated with low meaning. Social exclusion reduced purpose, value, efficacy, and optimism.

Noted Limitations and Future Directions
The first two studies the participants were excluded from strangers and believe that using closer friends would have a more significant effect. Future research could examine that the more you perceive meaningfulness could promote positive social interactions.

Zimmerman, A., and R. Easterlin. (2006). Happily Ever After? Cohabitation, Marriage, Divorces, and Happiness in Germany. Population and Development Review 32(3): 511-528.


Research concedes that marriage has a positive and lasting effect on well-being; however, the longevity of such hedonic gains is unclear. In a German panel study spanning fifteen years, psychologists’ argued the “setpoint theory,” stating that “people adapt quickly and completely to marriage” because marital partners return fairly quickly to their happiness level determined by their personality traits and genetics (Lucas, Clark, Georgellis, and Diener 2003). Emphasis on the impact of genetics and personality on happiness undervalues the potential influence of personal action or public policy on well-being.

The present study reconsiders the same data set analyzed by Lucas, et al. in 2003 (the German Socio-Economic Panel), covering 21 waves (1984-2004) compared to the prior study’s 15 wave (1984-1998). The sample is of first marriages among previously unmarried people, and data reflect life satisfaction two years prior to marriage and at least two years after marriage. Post-marriage data is crucial in examining whether there is a return to baseline satisfaction after the “honeymoon period,” as some research might suggest (Lucas et al., 2003). This study also considers the effects of subjective well-being on the formation of cohabiting unions prior to marriage.
Two key results of the Lucas et al. study are reconsidered. The first test considers if individuals still married two or more years into their first marriage exhibit a return to baseline life satisfaction. Second, tests are conducted to determine whether a significant increase in life satisfaction occurs around the formation of unions (e.g. cohabitation, marriage).

Zimmerman’s study from 2006 was identified as a key study because it is the longest-running panel to study subjective well-being.

A four-term hierarchical regression model was used to examine life satisfaction of married couples over time. The intercept reflects average life satisfaction of the participants in the “baseline” period: all non-cohabiting years prior to marriage (t1 and before). Therefore, the first term is a cohabitation term, measuring average difference in life satisfaction from one’s baseline value arising from cohabitation. The second term, a marriage “reaction” term, measures the average difference in life satisfaction in the first year of marriage (t0) and the next year (t+1) versus one’s baseline value. A marriage “adaptation” term measures the average difference in life satisfaction from one’s baseline value from the second year of marriage and all years thereafter (t+2 and on). Only one term for marriage is included in the “reaction” and “adaptation” terms to test the “honeymoon” effect. Finally, a “divorce” term measures the difference in life satisfaction from baseline values in individuals who divorce after two or more years of marriage.

The German Socio-Economic Panel contains questions that allow for examination of cohabitation and divorce in the sample of first marriages. 29% of partners cohabited before marriage, and 151 people divorced after two or more years of marriage. Divorces that occurred within the first two years of marriage (n= 2), marriages ended by the death of a spouse (n= 5), and marriages with a foreign-born spouse living in a foreign country (n= 6) were excluded. Data were also controlled for sex, age, income, education, employment, and religiosity, as these factors have been shown to significantly affect life satisfaction (Argyle 1999; Blanchflower and Oswald 2004b; Frey and Stutzer 2002).
Life satisfaction in the German Socio-Economic Panel is determined by responses to the question: “How satisfied are you with your life, all things considered?” Responses are ranked from 0 (completely dissatisfied) to 10 (completely satisfied), where life satisfaction scores are centered around the annual mean of each population subsample.

The baseline value of life satisfaction in this sample (0.10) does not differ significantly from that of the German population as a whole. As in previous studies, formation of cohabiting unions prior to marriage raises life satisfaction significantly from baseline values (0.183). During the first year of marriage, life satisfaction increased to a value of .369 above the baseline satisfaction level, but ratings decline to a value of 0.173 above baseline after the first year of marriage. These results confirm the “honeymoon period” effect on life satisfaction, followed by decline, presumably due to habituation. Nevertheless, married individuals are still happier, on average, than they were in their baseline period (Stutzer and Frey 2006, Table A2).

Findings suggest that the formation of successful unions–cohabiting or marital–positively affects well-being, but there is no significant difference in the life satisfaction effect of the two types of unions. Moreover, results show that individuals who remain married for two or more years do not revert to their baseline value of life satisfaction; thus contradicting the “setpoint theory.” These results differ from those of the Lucas et al. study due to original researchers’ failure to treat age as varying with time. Additionally, present results suggest that individuals who eventually divorce may be selected on personality characteristics which predispose the group to to significantly lower baseline satisfaction compared to their married counterparts.

Regarding limitations, uncontrolled variability in the data prohibits the extrapolation of data to prediction of long-term life satisfaction. Notably, the divorce subgroup differs from the first marriage group due to lower overall economic status and apparent selectivity regarding personality traits conducive to lower life satisfaction. This uncontrolled variance resulted in a slightly negative life satisfaction baseline value for this group.

With 21 waves, this study represents the longest-running panel study to date that includes a measure of subjective well-being. Results suggest that the formation of unions has a significant positive effect on life satisfaction, while the dissolution of unions through separation or divorce has a significant negative effect. These results are consistent with the “social support” interpretation commonly offered for the association between marriage and life satisfaction. Ratings of life satisfaction during cohabitation and two years into marriage did not differ significantly. Major implications of the study support a roots perspective of marriage dissolution, positive that divorce results from distinct socioeconomic and personality traits and not a disparate course of life satisfaction in early marriage.