1. Before the class starts: Login to a computer Read the Data analysis assignment 4 on MyCourses If you use Stata: Start Stata Start a new do file Open the PDF documentation about mixed If you use RStudio: Start RStudio Start a new R script

2. Data analysis assignment 4

3. Task Delmar and Wiklund (2008) study the relationship between the small business managers' growth motivation and firm growth. One of their hypotheses is Hypothesis 2: Growth motivation at T1 has a positive effect on growth at T2. Your task is to do a replication study using the provided data. The data are from the Orbis database and from a longitudinal survey of software companies in Finland. The data from the Orbis database are self-explanatory. The survey data are from question number 4 of the survey form, which is provided as a part of the data package: "How well do the following statements describe the growth of your firm?”

4. How to get your analysis file started Stata Load the data following the instructions Explore the data using e.g. describe, summarize, inspect, codebook, graph matrix, and stem RStudio Load the data using the read.dta from the foreign package Load the psych, car, effects, and texreg packages by adding library command to start of the R file. (If a package is not found, you need to install it) Explore the data using e.g. describe, lowerCor, corr.test, and scatterplotMatrix

5. How to submit your answer Stata Set your working directory Start your do file with log using assingment1, replace text End your do file with log close After each graph add graph export plotX.pdf Open the Word document template from MyCourses Copy-paste the content of assignment1.log to the document template and insert the exported figures into right places. In word, write comments in normal style and use headings where appropriate RStudio Compile a notebook in MS Word format In word, write comments in normal style and use headings where appropriate

6. Hypothesis 2: Growth motivation at T1 has a positive effect on growth at T2.

7. Workflow for a quantitative paper 1.Form hypotheses 2.Acquire data 3.Construct a database 4.Explore, filter, and transform data 5.Create composite measures 6.Descriptive statistics 7.The main analyses 8.Diagnostics for the main analyses 9.Supporting analyses 0. Research Topic 1. Formulation Research Question 2. Preparation of the research design 5. Data Collection 3. Measurement 4. Sampling 6. Data processing 7. Data analysis and interpretation

8. Delmar, F., & Wiklund, J. (2008). The Effect of Small Business Managers’ Growth Motivation on Firm Growth: A Longitudinal Study. Entrepreneurship Theory and Practice, 32(3), 437–457

9. Step 2: Acquire data

10. Step 3: Constructing a database You may have something like thisFor the analysis you need this Data row = observation Data column = variable Wide form data: each repeated measure is a new variable Long form data: each repeated measure is a new observation

11. Strategy 1: Excel

12. Strategy 2: Statistical software StataR

13. Step 4: Explore, filter, and transform data http://www.ats.ucla.edu/stat/stata/notes/exploring.htm http://www.ats.ucla.edu/stat/r/modules/exploring_w_graphics.htm R Stata

14. Step 5: Create composite measures Gen1 Gen2 Gen3 Risk1 Risk2 Intern1 Intern2 Intern3

15. Measurement validity “a test is valid for measuring an attribute if and only if (a) the attribute exists and (b) variations in the attribute causally produce variations in the outcomes of the measurement procedure.“ (Borsboom, Mellenbergh, & van Heerden, 2004, p. 1061) Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2004). The concept of validity. Psychological Review, 111(4), 1061-1071. Measurement validity is a simple concept. The process of showing validity – validation – is difficult A variable that we assume to exists but for which we do not have data is called a latent variable

16. Measurement validity No general proof for measurement validity exists: 1.If multiple measures correlate, they may measure the same thing But we do not know what the thing is And they may correlate for some other reason (endogeneity) 2.If a measure behaves as expected in relation to other measures, it may be valid Statistical model T x1 x2 x3 e e e

17. Realiability Triangulation: Multiple, independent measures Combination of multiple measures as a composite measure The degree to which the multiple measures are correlated can be used to assess reliability X 1i = T i + e 1i X 2i = T i + e 2i X 3i = T i + e 3i Where, X i is a person’s observed score on an item, T i is the 'true' score (i.e., what we hope to measure), e i is random error, or noise. Singleton, R.A.J. & Straits, B.C., 2005. Approaches to Social Research 4th ed., New York: Oxford University Press, pp. 121- 123

18. Reliability of a summed scale Cronbach’s alpha “Alpha is defined as the proportion of a scale's total variance that is attributable to a common source, presumably the true score of a latent variable underlying the items.” (DeVellis, 2003 p. 31) DeVellis, R. F. (2003). Scale Development: Theory and Applications (2nd ed.). SAGE Publications. Peterson, R. A., & Kim, Y. (2013). On the relationship between coefficient alpha and composite reliability. The Journal of Applied Psychology, 98(1), 194–198. doi:10.1037/a0030767

19. Wiklund, J., & Shepherd, D. A. (2009). The effectiveness of alliances and acquisitions: The role of resource combination activities. Entrepreneurship Theory and Practice, 33(1), 193–212.

20. Step 6: Descriptive statistics and correlations Deephouse, D. L. (1999). To be different, or to be the same? It’s a question (and theory) of strategic balance. Strategic Management Journal, 20(2), 147–166. Do the correlations make sense?

21. Step 7: Main analysis Rules of thumb: Start with linear regression Include first all controls in one model and then all study variables in second model Do diagnostics for the models Once you think that you have solved potential data issues, move to more complex models You typically need multiple models (Durand et al have 6, excluding robustness checks. Deephouse has 3 models)

22. 1.All relationships are linear 2.Independence of observations (No perfect collinearity and non-zero variances of independent variables) 4.Error term has expected value of zero given any values of independent variables 5.Error term has equal variance given any values of independent variables 6.Error term is normally distributed 21.6.2010 22 Step 8: Diagnostics for the analyses - OLS assumptions

23. Possible remedies for problems Data exclusions Outliers A subset of data may not be relevant -Hint: Some of the companies are part-time solo entrepreneurs. How would you identify them? Data transformations, particularly log transformation Alternative models and estimators Heteroskedasticity and/or cluster robust standard errors

24. Step 9: Supporting analyses Assess selection effect Compare the sample with population If being in the sample correlates with the study variables, you may have a problem Statistical remedies available (No need to do this for the assignment) Robustness checks (Internal validity) Run alternative models Reverse causality? Run models with subsets of data

25. After this, you only need to format the tables and write the paper