Are Generational Categories Meaningful Distinctions for Workforce Management? (2020) / Chapter Skim
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4 Review of the Generational Literature
4 Review of the Generational Literature
Pages 51-74
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From page 51... ...
. This chapter summarizes our findings and conclusions about the state of this body of research, referred to collectively here as generational research or generational literature.
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. With regard to attitudes, most empirical studies focus on job satisfaction, organizational commitment, and intent to leave (Costanza et al., 2012; Parry and Urwin, 2011)
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The current state of evidence suggests that any observed differences among workers are more likely to reflect age differences at the time of measure or evolving social and work conditions as a result of historical events impacting all people (see Chapter 2) than true generational distinctions.
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. Instead of studying social change, however, the bulk of the work-related generational research adapts these sociological theories to the study of individual attitudes and values.
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From page 55... ...
Using just birth years to define cohorts assumes that the influence of proximal historical events and social, cultural, and economic phenomena on those cohorts' individual members has already been established -- an assumption largely untested. Thus, the generational research tends to take as antecedent an undefined set of shared experiences assumed to have shaped the attitudes/values to be measured.
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From page 56... ...
It also looks at other methodological concerns involving measurement and sampling. Challenges of Separating Age, Period, and Cohort Effects Research aimed at identifying and determining the extent of generational influences in the workplace essentially tries to separate generation effects from age or period effects (see Box 4-1)
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From page 57... ...
at the time of exposure, whereas a period effect impacts all people regardless of age. A cohort effect is unique to people born in a particular year or set of years because of when in their development they were exposed to particular events.
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As discussed above, however, little theoretical work has examined the mechanisms that could be responsible for differences among generations. Further, it turns out to be a challenging task even to separate cohort effects from age and period effects.
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. The identification problem makes it challenging to design a study that can distinguish cohort from age effects or cohort from period effects without making certain assumptions.
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As discussed previously, such a design confounds age and cohort effects. Period effects are undetectable because all groups are completing the survey at the same time, and period effects are therefore constant.
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Measure of Work Centrality 0.8 0.7 0.6 Figure 4-1(c) 0.5 Age effect with period effect 0.4 from event at 2020 and 0.3 specific experience for 0.2 Cohort 2 between 2000 and 0.1 2010 and specific experience 0 for Cohort 3 between 2020 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 and 2030 Year of Measure FIGURE 4-1 Graphs of different outcomes for a hypothetical study of work as a central life interest over time.
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Such a systematic pattern would suggest changes in organizational commitment over time; however, it would be impossible to discern whether any observed changes were due to period or generation effects. Multilevel models applied to nested datasets -- Multilevel models are a family of statistical tools that are appropriate for studying databases in which some observations are nested within others, such as when data are collected from multiple individuals across multiple years.
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. Other Methodological Concerns As discussed in the previous section, cross-sectional surveys are typically not useful for studying generational differences because they confound age and cohort effects, and one of the most promising approaches for
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. Measurement Invariance Researchers interested in workplace characteristics typically focus on such topics as job satisfaction, intrinsic motivation, and organizational commitment, termed "constructs" in the social sciences.
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Many studies in the generational literature fail to test explicitly for measurement invariance, adding further ambiguity to attempts to draw conclusions from the existing literature. Only two studies included in the committee's review directly examine measurement invariance in the context of generational differences (Meriac, Woehr, and Banister, 2010; Twenge et al., 2010)
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However, these issues are relevant to the analysis of the strength of the evidence for generational differences. Consumers of generational research thus need to evaluate whether issues
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When researchers have the goal of identifying generation differences in certain domains, they often sample based on the birth cohort categorizations of various generations. Neverthe 3 A range of qualitative analytic approaches -- such as narrative, grounded theory, phenom enological, critical, discursive, case study, and thematic analysis approaches -- have been used in the generational literature (Lichtman, 2014)
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From page 68... ...
, an obvious issue with this approach is that neither self-identification with the targeted generational groups nor arbitrary categorization based on the span of birth years can rule out the confounding effects of age and period discussed earlier. Accordingly, even if systematic differences are seen in interview responses across the intended generational groups, it is unclear whether those differences are due to generation, age, or period effects.
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The committee's review of the generational literature revealed six studies employing both quantitative and qualitative methods. However, these studies appear to have the same weaknesses identified above -- insufficient internal and external validity in both the qualitative and quantitative portions to justify inferences about generational differences.
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statistical challenges in separating out age, period, and cohort effects, even with the more rigorous research designs. Together, these limitations call into question whether researchers can draw sound inferences from the existing literature.
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population in the various years and thus provide repeated value measurements across ages and time. The authors analyze these data using hierarchical logistic regression analyses in which period and cohort differences are modeled using random effects (i.e., a multilevel model applied to repeated surveys administered across multiple years)
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The contrast between these two studies is telling, showing that when more rigorous methods are used, what appears to be attributable to generation effects can actually be attributable to period effects. Unfortunately, very few studies examining worker attitudes and values have used APC methods.
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Such steps would include • decreased use of cross-sectional designs with convenience samples; • increased recognition of the fundamental challenges of separating age, period, and cohort effects; • increased use of sophisticated approaches to separate age, period, and cohort effects while recognizing any constraints on the inferences that can be drawn from the results; • greater attention to the use of samples that are representative of the target populations of interest; • greater attention to the design of instruments (e.g., surveys) to ensure that the constructs of interest (i.e., measured attitudes and behaviors)
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