The Inevitability of Multiple Levels of Analysis in Organizational Research and What to Do About It
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The Inevitability of Multiple Levels of Analysis in Organizational Research and What to Do About It

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The Inevitability of Multiple Levels of Analysis in Organizational Research and What to Do About It

The Inevitability of Multiple Levels of Analysis in Organizational Research and What to Do About It

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The Inevitability of Multiple Levels of Analysis in Organizational Research and What to Do About It The Inevitability of Multiple Levels of Analysis in Organizational Research and What to Do About It Document Transcript

  • The Inevitability 1The Inevitability of Multiple Levels of Analysis in Organizational Research, and What to Do About It Edgardo Donovan RES 620 – Dr. Wenli Wang Module 4 – Case Analysis Monday, December 5, 2011
  • The Inevitability 2 The Inevitability of Multiple Levels of Analysis in Organizational Research, and What to Do About It In response to the many challenges in measuring organizational behavior from issues ofindividual behavior within organizations hierarchical linear modeling (HLM) has emerged as atechnique to measure and analyze multilevel relationship models. It is too simplistic to say thatorganizations "behave" as though they were people, for either research or practice. HLMprovides for a more robust examination of models for data having two or more levels. It is aparticularly appropriate analytical strategy to employ because the focus is on the potentialrelationships of both individual and group-level variables. The majority of work in organizational behavior has been conducted at the individuallevel of theory, measurement and analysis. While the field of organizational behavior may beviewed as largely mixed-level, incorporating individual, group and organizational-levelphenomena, relatively few group-level and mixed-level theories with corresponding levels ofresearch exist (Schnake 2003, p. 283). When it is necessary to conduct research involving intra-group and individual dynamics there may be significant challenges in attempting to avoid overgeneralization. An example of this is the Atomistic Fallacy phenomena where micro researchersattempt to generalize findings from individual level studies to higher levels. E.g., finding thatsupervisors rated high in charisma perform better than those rated low in charisma does not meanthat team level relationship between leader charisma and team performance is similar (Eveland2011, p. 4). Modeling relationships can have single-level models describe the relationship amongvariables at a single level of theory and analysis, including individual level, team level and
  • The Inevitability 3organizational level models. Cross-level models describe the relationship among variables atdifferent levels of analysis. Homologous multilevel models specify that a relationship betweentwo variables holds at multiple levels of analysis (Eveland 2011, p. 11). Multilevel theory building presents a substantial challenge to organizational scholarstrained, for the most part, to "think micro" or to "think macro" but not to "think micro andmacro"-not, that is, to "think multilevel" (Klein 2003, p. 6). It is very difficult to separate andmeasure with great precision organizational behavior from issues of individual behavior withinthose organizations. It is too simplistic to say that organizations "behave" as though they werepeople, for either research or practice. The implications of such a theory are that group andindividual goals, motivations, and influencers are perfectly aligned. Typically there are differentgoals, motivations and influencers among people, groups of people, and their respectiveorganizations. Practitioners have a vested interest in attempting to better understand thosedynamics so that they enact policies geared towards harmonizing goals or creating a level ofsynergy between people and groups better focused on organizational interests. Typically, theymay rely on researchers to provide an analysis of organizational group dynamics at the firm,industry, or universal level to better understand where interests diverge, intersect and overlap.Traditionally, researchers testing multilevel models have had two data analysis options. The firstwas to assign the higher level measure to each unit at the lower level (e.g., assign group scores toindividuals), and then conduct analyses strictly at the lower level. The second alternative was toaggregate measures taken at the lower level of analysis (e.g., aggregating individual-levelmeasures to form group-level composites) and conducting analyses at the higher level only. Eachof these options has potential empirical and conceptual weaknesses. With the first option, the
  • The Inevitability 4researcher must assume that individual responses are not influenced by group characteristics.This approach yields biased estimates of the standard errors and increases the chance of Type Ierror. With the second option, statistical power often is an issue, as is the appropriateness ofinferences concerning relations among the aggregated variables (Klein, 1994). One of the organizational research techniques a researcher may utilize to measure andanalyze multilevel relationship models is hierarchical linear modeling (HLM). HLM provides fora more robust examination of models for data having two or more levels. It is a particularlyappropriate analytical strategy to employ because the focus is on the potential relationships ofboth individual and group-level variables (Kidwell 1997, p. 2). Hierarchical linear modeling(HLM) allows for the investigation of both within and between-group effects on an individual-level dependent variable through an empirical Bayesian estimation process in which twodifferent models are estimated iteratively. A within-group or "level-1" analysis is used toestimate two separate parameters describing the relationship between the predictors and the focaldependent variable within each group. These intercept and slope parameters obtained from thelevel-1 analysis serve as the dependent variables in equations used for a between-group or "level-2" analysis (Kidwell 1997, p. 7). Though from an analytical perspective hierarchical linear modeling (HLM) has a numberof advantages for multilevel research, some cautions should be noted. First, a number ofassumptions are required in making inferences from HLM modeling results. HLM proceduresinvolve certain assumptions about the data which when violated reduce the confidence withwhich statistical inferences may be made. The relatively higher complexity of random coefficientmodels typically used in HLM may make greater demands on these assumptions. For example,
  • The Inevitability 5with random independent variables, it becomes more difficult to satisfy the assumption thatindependent variables are uncorrelated with errors in model equations, which in turn hasramifications for the assumption of multivariate normality underlying HLM. Second, despite itspotential advantages, HLMs random coefficients approach may be unnecessary for somemultilevel data structures. Traditional techniques perform as well or better if there are largegroups and small intraclass correlations, and the interest is only in fixed-level regressioncoefficients. Lastly, with HLM, as with other analytical procedures, researchers must be mindfulof power issues when designing and interpreting their results. HLM level-1 parameter estimatesrequire an adequate number of observations, just as other estimation procedures do. But inaddition, level-2 parameters require an adequate number of units, with the number of within-units observations being of minor importance (Kidwell 1997, p. 15). In response to the many challenges in measuring organizational behavior from issues ofindividual behavior within organizations hierarchical linear modeling (HLM) has emerged as atechnique to measure and analyze multilevel relationship models. It is too simplistic to say thatorganizations "behave" as though they were people, for either research or practice. HLMprovides for a more robust examination of models for data having two or more levels. It is aparticularly appropriate analytical strategy to employ because the focus is on the potentialrelationships of both individual and group-level variables.
  • The Inevitability 6 BibliographyKidwell, RE, Mossholder, KE, and Bennett, N. (1997) Cohesiveness and organizationalcitizenship behavior: a multilevel analysis using work groups and individuals. Journal ofManagement. Nov-Dec issue.Klein, Katherine & Kozlowski, Steve (2003) A multilevel approach to theory and research inorganizations: contextual, temporal, and emergent processes. Chapter 1 in Multilevel Theory,Research, and Methods in Organizations: Foundations, Extensions, and New DirectionsSchnake, M. and Dumler, M. (2003). Levels of measurement and analysis issues inorganizational citizenship behaviour research. Journal of Occupational and OrganizationalPsychology. 76(3):283