field as they otherwise might, had they included a more complete measurement of all major components.
Another limitation of current research in the area of subjective well-being is an overreliance on cross-sectional research designs, particularly those using a sin-gle methodology (e.g., self-report questionnaires only). In a typical subjective well-being study using a cross-sectional design, data are gathered at one point in time, perhaps by handing out a set of questionnaires to a psychology class, a work group, or at a senior center. The respondents complete the questionnaires and return them to the researcher. The resulting data are then analyzed, and the mea-sure of subjective well-being is perhaps found to be correlated to some other variable that has been assessed (e.g., a personality characteristic). If the correlation is large or otherwise interesting, the results are likely to be reported in some form. Depending on the outcome of the study, additional studies might be con-ducted. Cross-sectional studies can be very useful, particularly in the initial stages of a research program (e.g., developing a new measure). But cross-sectional research also involves some significant limitations, particularly for research on subjective well-being.
One issue involves the effects of transient mood states and other contextual factors that might influence response to the subjective well-being measure. These momentary factors can, under some circumstances, exert a significant influence on how an individual responds to a measure of SWB (Schwarz & Strack, 1999).
Because data gathered using a cross-sectional design can provide information only on a respondent’s experience at a single moment in time, it is possible that this response might be influenced by some transient factor at the particular moment of response and therefore be a less valid indicator of that individual’s general or global level of subjective well-being. This potential threat to the valid-ity of assessment is particularly an issue with brief, single-item subjective well-being measures, but it can also be a factor even with multiple-item scales.
Although these transient mood and contextual effects appear to be limited (Pavot
& Diener, 1993a; Eid & Diener, 2004; Schimmack & Oishi, 2005), it is desirable to avoid them nevertheless; with the most simple form of a cross-sectional design, this is not possible.
One potential way to avoid transient mood and contextual effects is the use ESM, as described in a previous section. But ESM is not feasible for cross-sectional designs because it requires repeated experiential reports across days or perhaps weeks. A longitudinal design is required in order to execute ESM effec-tively.
If a longitudinal design is impractical, an alternative approach that would allow researchers to validate self-reports of subjective well-being from a cross-sectional study would be to use a multiple-method design. Informant reports, memory measures, or other non-self-report measures of subjective well-being can be incorporated within a study to provide external validation to self-reported
measures of subjective well-being. Self-reported and non-self-report measures of subjective well-being could also be combined to produce a standardized compos-ite score of subjective well-being. Such a technique would tend to reduce the potential impact that transient mood and contextual effects might exert on the self-reported measures.
A second issue related to the use of cross-sectional designs involves the anal-ysis and interpretation of the data. Because experimental manipulations are rarely utilized in subjective well-being research, the approach to statistical analysis of the data from these studies is predominantly correlational in nature. Most often, measures of subjective well-being are examined to determine their relation to other variables (e.g., a correlation coefficient might be computed between a measure of PA and a measure of the personality trait of extraversion). When such a correlation coefficient is computed based on data from a cross-sectional design, it can reveal the strength and direction of a relationship between the two vari-ables, but it cannot be used to determine a causal relationship. That is, such a correlation might inform us that the relation between PA and extraversion is positive and moderately strong, but it cannot tell us whether PA is “causing”
extraversion, or vice versa. A measure of extraversion can be used to predict PA, or vice-versa, but the causal dynamics of the relation cannot be determined.
In contrast, data obtained from a longitudinal design can often be used to establish the causal pathway more clearly. An example of this process can be seen in the research on the relation between subjective well-being and marital status.
People who do marry typically report higher levels of subjective well-being than those widowed, divorced, or single (Mastekaasa, 1994; Diener et al., 1999). But until recently, the causal direction was unclear. Was marriage “causing” happi-ness, or were happier people more likely to marry? By adopting longitudinal designs, two large-scale studies (Lucas, Clark, Georgellis, & Diener, 2003; Marks
& Fleming, 1999) have demonstrated that people with initially higher levels of subjective well-being are more likely to marry by the time of a later measure-ment occasion. Although it is a reasonable assumption that the benefits of mar-riage do, to some degree, contribute to subjective well-being after marmar-riage has occurred, it is clear that those with higher levels of subjective well-being are more likely to marry. Thus, a correlational relation observed in cross-sectional research was further articulated with longitudinal data.
A third factor that has hindered the advancement of subjective well-being assessment involves the lack of a systematic effort among researchers to move toward a refinement of the measurement process. The absence of a common protocol or set of standards for the development and use of subjective well-being measures has contributed to the “haphazard” condition of the current database, as described by Diener and Seligman (2004, p. 4). The uneven psychometric qual-ity of the measures, combined with other methodological and sampling issues, have the effect of reducing confidence in the findings. These factors make efforts
to establish general conclusions about subjective well-being, by summarizing the findings of diverse studies, risky at best.
In an effort to move toward a more refined approach to subjective well-being assessment, Diener (2005) has proposed a set of guidelines and recommen-dations regarding the development and use of measures of both subjective well-being and ill-well-being, and these proposed standards have received the endorsement of many researchers involved in research related to subjective well-being. The guidelines include recommendations regarding the psychometric quality of sub-jective well-being measures and the methodology by which these measures might be most effectively employed, along with definitions of subjective well-being and its major components. The guidelines are intended to establish a basis for the development of national indicators of subjective well-being, but they are generally applicable to basic research settings as well.
Taken together, the three factors discussed above have combined to slow the establishment of a dependable and valid database from the findings of subjec-tive well-being research. General conclusions regarding the causes and conse-quences of subjective well-being have been slow to emerge, in part because many studies have only or narrowly assessed subjective well-being, because high-quality, multiple-method longitudinal studies are rare, and because of a lack of concerted effort by researchers to refine the assessment process.
Fortunately, remedies for all of these shortcomings are at hand. Valid and reliable measures and innovative methodologies, such as ESM, are currently available. Examples of systematic longitudinal designs have been published in recent years, and these studies can serve as a basis for future efforts. At least one attempt to organize and systematize subjective well-being assessment (Diener, 2005) has been made, and this effort appears to be well received. Thus, the potential to improve the quality of subjective well-being research and to more firmly establish the database seems good. Conducting high-quality, longitudinal research is neither cheap nor easy, but its value in terms of knowledge base is high. Cross-sectional studies can clearly serve an important function, particularly at the earliest stages of a program of research, but their power to fully explain the dynamic processes related to subjective well-being is limited.