Doing Quantitative Research 26E02900, 6 ECTS Cr.

1. Doing Quantitative Research26E02900, 6 ECTS Cr. Olli-Pekka Kauppila Daria Volchek Lecture IV - May 21, 2014

2. Learning objectives – PM session Understand what multilevel research is and why it is relevant in management studies Acquire the elementary understanding of how multilevel modeling works Learn to interpret and evaluate studies using multilevel research methods

3. What is multilevel management research?

4. What is marine biology about?

5. What is marine biology about? “…the study of marine organisms and their environment” (Source: website of Department of Ecology and Evolutionary Biology, UCLA)

6. Will you be a marine biologist interested in oceans but not its inhabitants?orWill you be a marine biologist interested in whales but not their habitat?

7. What is management studies about?

8. Are you interested in organizations but not their organizational members?

9. Example: Upper echelons research Souitaris, V., & Maestro, B. M. M. 2010. Polychronicity in top management teams: The impact on strategic decision processes and performance of new technology ventures. Strategic Management Journal, 31: 652–678.

10. Strategic management research • The links between firm strategies, structures, and business environments are relatively well-understood • However, we do not fully understand how strategies are implemented and how they influence behaviors and outcomes within the organization • Organizations without people?

11. Are you interested in organizational members but not their organizational context?

12. Example: Research on employee turnover Chau, S. L., Dahling, J. J., Levy, P. E., Diefendorff, J. M. 2009. A predictive study of emotional labor and turnover. Journal of Organizational Behavior, 30: 1151-1163.

13. Organizational behavior research • The processes involving individual-level antecedents, behaviors, and outcomes are relatively well-understood • We do not fully understand how the social composition of organizations and work groups, as well as the larger cultural context, influences individual-level processes and outcomes People without organizations?

14. Motivation for multilevel approach in organizational research “Due to the inherently hierarchical nature of organizations, data collected in organizations consist of nested entities” (Hofmann, 1997) “Adopting either a micro or a macro stance yields an incomplete understanding of behaviors occurring at either level” (Hitt et al., 2007)

15. How does it work?

16. Data in multilevel research 29individuals

17. Data collected in organizations consists of nested entities – individuals within groups 29 individuals, individuals nested within 7 work groups

18. Data collected in organizations consists of nested entities – groups within firms 29 individuals, individuals nested within 7 work groups, work groups nested within 2 firms

19. Or… for example, firms, regions, and countries 29 firms, firms nested within 7 regions, regions nested within 2 countries

20. Classroom exercise I Read the introduction of Hitt et al.’s (2007) article Thinking about your study topic, or a topic that you could be interested in studying: • Could your data be hierarchically structured? • What are the relevant hierarchical levels? • What variables you have at each level of your model? • Share your thoughts!

21. Null model Level 1: Individual Job satisfaction LEVEL 1 MODEL JSATij = β0j + rij LEVEL 2 MODEL β0j = γ00 + U0j

22. Null model Level 1: Individual Job satisfaction As there are no predictors at level 1, all variance within groups goes to the error term As there are no predictors at level 2, all variance between groups goes to the error term LEVEL 1 MODEL JSATij = β0j + rij LEVEL 2 MODEL β0j = γ00 + U0j

23. Null model Level 1: Individual Job satisfaction As there are no predictors at level 1, all variance between level 1 cases goes to the error term As there are no predictors at level 2, all variance between groups goes to the error term LEVEL 1 MODEL JSATij = β0j + rij LEVEL 2 MODEL β0j = γ00 + U0j

24. Inter-classcorrelation (ICC1) What proportion of the variability in JSAT is between groups and what proportion is between individuals within groups? ICC1 =τ00/(τ00+σ2) = The amount of variance that resides between groups To find out, we use the values from the null model: ICC1 = Between group variance in JSAT (U0j) / (Between group variance in JSAT (U0j) + within group variance in JSAT (rij)), or In most cases, ICC > 0.10 is desirable

25. Adding Level 1 predictor Level 1: Individual General self-efficacy (GSE) Job satisfaction LEVEL 1 MODEL JSATij = β0j + β1j(GSE) + rij LEVEL 2 MODEL β0j = γ00 + U0j β1j= γ10 + U1j

26. Adding Level 1 predictor Level 1: Individual General self-efficacy (GSE) Job satisfaction Between-group variance affects both the intercept (constant) and the coefficient of Level 1 predictor (i.e. GSE) LEVEL 1 MODEL JSATij = β0j + β1j(GSE) + rij LEVEL 2 MODEL β0j = γ00 + U0j β1j= γ10 + U1j

27. Adding Level 2 predictor Outcome-based control (OBC) Level 2: Firm Level 1: Individual General self-efficacy (GSE) Job satisfaction LEVEL 1 MODEL JSATij = β0j + β1j(GSE) + rij LEVEL 2 MODEL β0j = γ00 +γ01 (OBC) + U0j β1j= γ10 + U1j

28. Adding Level 2 predictor Outcome-based control (OBC) Level 2: Firm Level 1: Individual General self-efficacy (GSE) Job satisfaction The level of JSAT is no longer explained only by between-group error variance, but also by a measured level 2 variable LEVEL 1 MODEL JSATij = β0j + β1j(GSE) + rij LEVEL 2 MODEL β0j = γ00 +γ01 (OBC) + U0j β1j= γ10 + U1j

29. Adding cross-level moderator Outcome-based control (OBC) Level 2: Firm Level 1: Individual General self-efficacy (GSE) Job satisfaction LEVEL 1 MODEL JSATij = β0j + β1j(GSE) + rij LEVEL 2 MODEL β0j = γ00 +γ01 (OBC) + U0j β1j= γ10 + γ11 (OBC) + U1j

30. Adding cross-level moderator Outcome-based control (OBC) Level 2: Firm Level 1: Individual General self-efficacy (GSE) Job satisfaction In this model, OBC influences both the level of JSAT (= intercept) and the relationship between GSE and JSAT (coefficient of GSE) LEVEL 1 MODEL JSATij = β0j + β1j(GSE) + rij LEVEL 2 MODEL β0j = γ00 +γ01 (OBC) + U0j β1j= γ10 + γ11 (OBC) + U1j

31. Data requirements Must capture the hierarchical structure! As a rule data, collect data on 2 or 3 levels How many participants are needed? • Level 2 (e.g. groups or firms): usually at least 20-30 participants • Level 1 (e.g. individuals): 10-30 per each participant at Level 2 • N at Level 1 tends to range between 400 and 1.000 • The usual rules apply • Caution with self-reports • Multiple informants are better than single informants • Having different data sources (objective & subjective) is a plus • Time-lag(s) between independent and dependent variables

32. Method and software for the analyses Hierarchical linear modeling (HLM) – also known as random coefficient model Use for 2-level or 3-level models Software • HLM (byScientificSortware of International) • Mplus • Others…

33. Example

34. Research question What kind of organizational structure and strategy facilitates organizational members’ citizenship behavior?

35. Hypotheses Hypothesis 1: Cross-functional integration is positively related to organizational members’ organizational citizenship behavior (OCB) Hypothesis 2: Emergent strategy-making is negatively related to organizational members’ OCB Hypothesis 3: The relationship between emergent strategy-making and OCB will be more negative for members whose learning orientation is higher rather than lower

36. Theoretical model Cross-functional integration Hypothesis 1 + Emergent strategy-making Hypothesis 2 – Firm-level Individual-level Hypothesis 3 – Learning goal orientation OCB

37. Measures Organizational citizenship behavior Indicate how often you engaged in the behavior during the past 12 months (1 = “never”; 5 = “always”) • Defended the organization when other employees have criticized it • Showed pride when representing the organization in public • Expressed loyalty toward the organization • Took action to protect the organization from potential problems • Demonstrated concern about the image of the organization

38. Measures Cross-functional integration • We freely communicate information about our successful and unsuccessful customer experiences across all business functions • All of our business functions are integrated in serving the needs of our target markets • All of our managers understand how everyone in our business can contribute to creating customer value • All functional groups work hard to thoroughly and jointly solve problems

39. Measures Emergent (as opposed to deliberate) strategy-making • We typically don't know what the content of our business strategy should be until we engage in some trial and error actions • My business unit's strategy is carefully planned and well understood before any significant competitive actions are taken (R) • Formal strategic plans serve as the basis for our competitive actions (R) • My business unit's strategy is typically not planned in advance but, rather, emerges over time as the best means for achieving our objectives become clearer • Competitive strategy for my business unit typically results from a formal business planning process (i.e., the formal plan precedes the action) (R)

40. Measures Learning goal orientation • I want to learn as much as possible from my job • I hope to gain a broader and deeper knowledge of my job as continue in this position • I desire to completely master my job • In my job, I prefer tasks that arouses my curiosity, even if they are difficult to learn • In my job, I prefer tasks that really challenge me so I can learn new things

41. Control variables Control variables at level 1: GENDER (1 = Female; thus, 0 = non-female) AGE_LN (= Natural logarithm of employee age in years) H_LEVEL (1 = Employee, 2 = Team leader, 3 = Management, lower level, 4 = Middle management,5 = Top management) Control variables at level 2: In this case, we did not include any (we could have, though)

42. Data Level 1: Individuals (N = 638) Level 2: Firms (N = 34) Level-1 data is based on the self-reports of organizational members at two data collection points Level-2 data is based on the responses of managers representing the firm (mean value of an average of 5.6 managers per firm) Variables at Level 1 collected at time 1: Learning goal orientation, control variables Variables at Level 1 collected at time 2: OCB Variables at Level 2 collected at time 1: Cross-functional integration, Emergent strategy-making

43. Preparations for analyses • Make sure you have 2 separate SPSS data files (Level1.sav & Level2.sav) (or 3 if you are making a 3-level model) Deal with missing data • All datasets must be complete (i.e. no missing values) • Recommended approach is multiple imputation • Variables in Level 1 file: FirmID, BossID, GENDER, AGE_LN, H_LEVEL, LEARNOR, OCB • Variables in Level 2 file: FirmID, INTEGRAT, EMERGE • The program uses the variable “FirmID” to match firms and employees working in firms

44. ID variable: FirmID Level 1 file: Level 2 file:

45. To investigate the appropriateness of multilevel modeling, start by examining a null model… OR: LEVEL 1 MODEL OCBij= β0j + rij LEVEL 2 MODEL β0j = γ00 + U0j

46. Nullmodel for computing ICC1 ICC1 = 0.08810/(0.08810+0.46340) = 0.1597 ICC1 =τ00/(τ00+σ2) = The amount of variance that resides between firms Use the values from the null model: ICC1 = Between group variance in OCB (U0j) / (Between group variance in OCB(U0j) + within group variance in OCB(rij))

47. Variance partitioning - recognizing the partial interdependence of individuals Firm level explanations Organizational citizenship behavior 16.0 % 84.0 % Individual level explanations Variance residing between individuals within firms = 1.00 - ICC1 = 84.0 %

48. Centeringdecisions In general, it is appropriate to center (i.e. standardize) independent variables to avoid multicollinearity There are two basic options for centering Level 1 variables: • Grand-mean centering: the grand mean (i.e. mean value of all level 1 cases) of the level-1 predictor is subtracted from each level-1 case • Group-mean centering: the group mean (i.e. mean value of all members of the same group) of the level-1 predictor is subtracted from each case

49. Example of centering Mean value of Learning orientation across all Level-1 participants is 3.05 (= Grand mean) Mean value of employees’ Learning orientation in Firm Omega is 3.96 (= Group mean) An employee called ”Pekka” works in Firm Omega and his Learning orientation is 3.42 Pekka’s Learning orientation after centering: • With Grand mean centering: 3.42 - 3.05 = 0.37 • With Group mean centering: 3.42 - 3.96 = -0.54

50. Centering must be guided by theory Consider that high salary increases organizational commitment But, which one is more relevant: • That the salary is high in general → grand mean center salaries • That the salary is higher than that of other members of the same organization → group mean center salaries • There are no obvious answers, you must provide the justification