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QUANTITATIVE METHODS IN IB RESEARCH

QUANTITATIVE METHODS IN IB RESEARCH

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QUANTITATIVE METHODS IN IB RESEARCH

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  1. QUANTITATIVE METHODS IN IB RESEARCH Kaisu Puumalainen Lappeenranta University of Technology Tel. 05- 621 7238, 040-541 9831 kaisu.puumalainen@lut.fi

  2. INTRODUCTION

  3. After the course, you can… • critically evaluate the research design and results of empirical studies • design an international large-scale survey • use databases to collect literature and data • develop valid and reliable measures for abstract constructs • recognize the main problems in cross-cultural studies • understand the applicability of the most typical quantitative analysis methods • use SAS software for analysing data • write a master’s thesis based on quantitative empirical data

  4. Timetable • 15.2. introduction, research process, reporting • 26.2. databases • 15.3. research design • 22.3. research design • 29.3. international issues • 9.4. assignment 1 DL • 12.4. analysis methods • 14.4. exam • 19.4. introduction to SAS • 26.4. analysis with SAS • 7.5. assignment 2 DL • 14.5. exam resit, if needed

  5. Proposals & theses to review • Part I: review the two research proposals • Write down a report of 2-5 pages • You can do the report together with another student • A list of issues to be covered is on the following slide • Structure the report e.g. as follows: • Description and evaluation of proposal I • Description and evaluation of proposal II • Comparison of the two proposals • Part II: evaluation of the two theses • Give grades 1-5 for each area and complement with max 1 page description of the strengths and weaknesses • DL 9.4.2010, return to kaisu.puumalainen@lut.fi

  6. Review of the proposals • Overall structure, are all relevant issues covered? • Problem specification • Empirical context of the study (country, industry, firm size), fit with problem? • Research approach and data collection (method, sampling, informant) • Operationalization of key concepts • Analysis methods (choice, reporting) • Biases, reliability and validity • Formalities (references, writing, etc.)

  7. Research proposal • Title • Background • The research problem and research objective(s)/question(s) (which further can be divided into sub objectives/questions) • Literature overview (What literature and studies are available of the subject? How this study is positioned to these research streams, and whether a research gap exists?) • Preliminary theoretical framework (What area(s) of business theory does the research topic belong to.) • Definitions (of special terminology used in the thesis) • Limitations and scope (what issues will be excluded and for what reason) • Method of research • Structure of the research • Tentative table of contents of the final report • Available source material • Tentative time table • http://www.des.emory.edu/mfp/proposal.html • http://www.statpac.com/research-papers/research-proposal.htm

  8. Evaluation of theses: areas to grade • Definition of the research problem • Positioning to existing research • Concepts, models, hypotheses& frameworks • Data collection • Analysis • Discussion, interpretation of results • Balanced structure of the report • Systematic and logic of the report • Thoroughness • Independence, criticality and effort • Reporting style • Readability

  9. Evaluation of theses: scale • 1 = weak • 2 = mediocre • 3 = satisfactory • 4 = good • 5 = excellent

  10. Data collection and analysis exercise • DL 7.5. • Graded 0-5, forms 25% of final grade • Pairwork • Data collection starts on 26.2. and more detailed instructions will be given

  11. REPORTING A QUANTITATIVE STUDY

  12. Reporting a quantitative study • Structure of the report/article: • Introduction • Theoretical part (including framework + hypotheses) • Methodology (sampling + data collection + measures + analysis) • Results (descriptive + testing) • Discussion (evaluation + implications) • Conclusion (limitations + further research)

  13. Introduction • Relevance of the topic • Practical reasons • Academic interest • Research gap and research questions • Overall literature review • It has not been done yet, why should it be done • How are we going to fill the gap in this study • Clearly articulate the study’s contributions

  14. Literature search • Article databases • ABI, EBSCO, Elsevier, Emerald, JSTOR, Springer, Wiley • http://www.lut.fi/fi/library/databases works through VPN • Citation information • ISI Web of Science, ISI JCR • http://www.lut.fi/fi/library/databases works through VPN • Google Scholar • http://scholar.google.fi/

  15. Literature review • Stand-alone and embedded reviews • Literature search (leading journals, databases, reference lists, web of science for forward citations, conference proceedings, working papers, books, managerial journals) • Start reading (find key articles, reviews, meta-analyses, date order, key author order) • Create a concept matrix, tables

  16. Literature review • Analyze the literature • History and origins of the topic • Main concepts • Key relationships of the concepts • Research methods and applications • Identify key contributions, strengths and deficiencies or inconsistencies • Synthesize • A research agenda • A taxonomy • An alternative model or conceptual framework

  17. Articles on conducting a lit review • Torraco, R.J. (2005) Writing integrative literature reviews: Guidelines and examples, Human Resource Development Review, 4 (3):356-367 • Webster, J. & Watson, R.T. (2002) Analyzing the past to prepare for the future: Writing a literature review, MIS Quarterly, 26 (2):13-23 • Rowley, J.& Slack, F. (2004) Conducting a literature review, Management Research News, 27 (6):31-39 • Gabbott, M. (2004) Undertaking a literature review in marketing, The Marketing Review, 4:411-429

  18. Development of hypotheses • Three sources: • Theoretical explanation for ”why?” (must always be there) • Past empirical findings (optional, from same or related fields) • Practice or experience (optional)

  19. Reporting the methodology 1 • sample: • Population specifications, sampling frame, size • Informant(s), method, process • Data collection: • Choice of data collection method, process, instrument development, pre-testing • Response rate, representativeness

  20. Example: data collection (1) The empirical data used in this study is drawn from a dataset collected using a structured mail questionnaire. The survey was carried out in spring 2004. The initial population consisted of Finnish companies engaged in R&D from eight different industry categories: food, forestry, furniture, chemicals, metals, electronics, information and communications technology (ICT), and services. The questionnaire was developed partly by using extant measurement scales, which were translated into Finnish. The use of a back-translation procedure involving a native English speaker ensured that the meanings of the item statements were not altered. Seven-point Likert scales were mainly used to minimize executive response time and effort (Knight & Cavusgil 2004). Pretests for getting feedback regarding the clarity of the survey items were conducted with ten companies of varying size in different sectors. Like numerous other researchers, we chose to rely on single key informants in our data collection. In order to maximize the data accuracy and reliability, we followed Huber and Power’s (1985) guidelines on how to get quality data from single informants. Entrepreneurial orientation is normally operationalized from the perspective of the CEO (Covin & Slevin 1989; Wiklund & Shepherd 2003), and CEOs are typically the most knowledgeable persons regarding their companies’ strategies and overall business situations (Zahra & Covin 1995). Most of our respondents had titles such as chief executive officer, managing director, chief technology officer and R&D director, indicating a senior position in the firm.

  21. Example: data collection (2) A total of 1140 companies were identified from the Blue Book Database. Of those, 881 were reached by telephone and were found eligible to answer questionnaire. Other firms were not reached in spite of numerous telephone calls, or were considered ineligible. Eligibility and the identity of the most suitable key informants were ascertained during the telephone conversation. Participation in the survey was solicited by means of incentives such as the offer of a summary report of the results, and by assuring confidentiality of the responses. Of the firms contacted by telephone, 200 refused to participate. The survey questionnaire, along with a preaddressed postage-paid return envelope and a cover letter describing the purpose of the research, was mailed to the 681 firms that agreed to participate. A reminder e-mail was sent to those who had not answered within two weeks.

  22. Example: data collection (3) A total of 299 responses were received, yielding a satisfactory effective response rate of 33.9% (299/881). Non-response bias was assessed on a number of variables (e.g., size, profitability, time of latest new product launch, international operation mode) by comparing early and late respondents, following the suggestions of Armstrong and Overton (1977). There was no evidence of non-response bias, with the exception that the firm size of the early and late respondents differed slightly: it was larger in the late-respondent group when measured against the number of employees (the sample means for the early and late respondents were 140 and 205 employees, t= -2.50, d.f.=121, sig.=.014). We also compared the distribution of the number of employees in our data with the corresponding distribution of all Finnish companies with more than 50 employees, and found that in the categories between 100 and 999 employees, the proportions were equal. Four per cent of firms have more than 1000 employees (Statistics Finland 2004), as did 13% of our sample. This suggests that very large companies may be over-represented, and is in contrast with the comparison of early and late respondents implying that companies with large numbers of employees might be under-represented. Furthermore, as there was no significant difference between the early and late respondents in terms of turnover, we concluded that our sample was not biased.

  23. Example: data collection (4) In order to minimize social desirability bias in the measurement of constructs, it was emphasized in the cover letter that there were no right or wrong answers, and that the responses would remain strictly confidential (Zahra & Covin 1995). The respondents were asked to recall the situation in their companies during the most recent three year period to avoid recollection errors. The sample used in this paper includes 217 firms from manufacturing and service segments. Seven different industry sectors were selected in aim to obtain a heterogeneous sample so as to increase the generalizability of the findings. Since we want to make distinction between individual and firm-level factors and in this study we aspire for capturing firm-level entrepreneurship and rather formal organizational renewal capabilities, the size class was restricted to firms with 50 employees or more. The upper cut-off 1000 employees was used to filter the largest firms out. This was done because the measures used to assess hypothesized relationship between independent and dependent variables include questions concerning organizational changes and international performance during the last three years. It is presumable that due to the organizational inertia in very large firms the lag between organizational changes and enhanced performance is longer than in small firms. Thus, it is possible that to capture the impact of organizational changes on performance of very large firms, the time period should be longer than used in this survey. To avoid the possible bias in results, the largest firms were omitted from this study.

  24. Reporting the methodology 2 • measures: • Measure development, control variables • validity and reliability • analyses: • What analysis methods were applied for testing the hypotheses • Validation and generalizability? • The choices and statistics to be reported vary by analysis method

  25. Example: measurement (1) Dependent variables: international performance We agree with many other authors (e.g., Cavusgil & Zou 1994; Katsikeas et al. 2000) that international performance is a multidimensional construct that should be measured using a variety of indicators (for a thorough review of the measures used, see e.g., Zou & Stan 1998; Leonidou et al. 2002; Manolova & Manev 2004). These indicators could be objective or subjective, absolute or relative, reflecting either the scale of international operations or success in them. We measured the scale of international operations on two objective indicators: 1) international sales as a percentage of total sales, and 2) the number of countries in which the company operates. These are both among the most commonly used proxies in this context (Walters & Samiee 1990; Sullivan 1994; Robertson & Chetty 2000; Autio et al 2000). In their review of 31 performance studies, Walters and Samiee (1990) found that 68% of them used the first and 13% the second measure. We also computed objective relative measures of the degree of internationalization by standardizing the international sales percentage and number of countries within each industry. These relative measures gave results that were identical to the absolute measures, and are thus not reported separately. We acknowledge that growth measures would be useful objective indicators of international performance as well. Autio et al. (2000) examined change in international sales as a percentage of total sales and growth in total sales, in order to understand the overall impact of growth in international sales. The success of international operations was assessed in a subjective manner. The respondents were asked to indicate their level of satisfaction with their international activities during the previous three years on six different dimensions of performance, and as a whole. The average of these seven items was also used as an overall indicator (Cronbach alpha = .91).

  26. Example: measurement (2) Our reliance on self-reported data from single informants introduces the risk of common method variance. In order to obviate this risk, we followed the procedure suggested by Wiklund and Shepherd (2003) and computed the correlation coefficient with a self-reported profitability measure and an externally obtained one. We were able to find the return on investment (ROI) figures of 68 respondent companies from Talouselämä and Tietoviikko magazines, which are Finnish business magazines that collect and publish annual financial data from several industries. The correlation between the measures was .40 (p<.01). In fact, the results of previous research suggest that subjective measures of performance can accurately reflect objective measures (Lumpkin & Dess 2001).

  27. Example: measurement (3) Independent variables Entrepreneurial orientation was conceptualized as consisting of the dimensions of innovativeness, proactiveness and risk-taking. The measure was adapted from Naman and Slevin (1993), and Wiklund (1998), which were based on measures developed in Covin and Slevin (1988) and Miller and Friesen (1982). Pretests were conducted, after which some original items were dropped and new ones generated on the basis of previous studies on firm-level entrepreneurship. The measure included nine items, which were assessed on a scale from one to seven (see Appendix). The three dimensions are closely related, so a composite measure was constructed as an average of all nine items, resulting in a reliability coefficient of .74, which is satisfactory according to the guidelines presented in Nunnally (1978).

  28. Example: measurement (4) Control variables There are firm-specific and external factors that may affect a firm’s international performance, regardless of its strategic orientation (Lumpkin & Dess 1996) or its renewal capability. We therefore controlled for firm size, experience in international operations, and environmental dynamism. Firm size is normally operationalized as the number of employees and/or amount of annual sales. It is assumed to affect international performance positively, as a larger firm has a larger pool of resources to exploit and the possibility to achieve advantages of scale in its international operations. In order to avoid problems of multicollinearity in the hypothesis testing, we only used annual sales turnover (reported in million €) as an indicator of firm size. The sales were log-transformed to correct for positive skewness.

  29. Reporting the results: descriptive • Graphics • Bar, histogram • pie • Line and area • scatter • Frequency tables • Descriptive statistics (in a table) • N • Mean, median • Standard deviation, min, max • Above statistics for non-transformed variables • (Skewness, kurtosis) • Correlation matrix (for transformed variables)

  30. example

  31. example Significance a p < .05, b p < .01

  32. Reporting the results: testing • Varies by analysis method • Model fit statistics • Test statistic (+ standard error) and significance level or confidence interval • Mention that basic assumptions were checked for • (Power of the tests) • No software output as such • Use tables!!

  33. Example: The hypotheses were tested by hierarchical linear regression analysis. In the base model, only the control variables (ln-transformed sales, ln-transformed years of international experience and environmental dynamism) were entered into the regression model. The hypothesized independent variables (entrepreneurial orientation, number of reconfiguring activities, and success in reconfiguring activities) were then added in the second phase. The hypothesized effects would then be significant only if the increase in the coefficient of determination after the base model was large enough and the regression coefficients of the hypothesized variables in the effect model were statistically significant. The use of the hierarchical model thus directly shows the increase in predictive power that can be attributed to the hypothesized variables over and above the effects of the control variables. The results of the regression analyses are presented in Table 3.

  34. Example:

  35. Reporting the results: discussion and conclusions • Avoid numbers here, state clearly what the results mean • Bring up the results that were surprising, new or important • Compare with earlier empirical studies, it is good to get some similar results, and something new • If your results conflict with earlier ones, try to explain why • Comment on the stability, generalizability and accuracy of the results • Limitations (e.g. Research design, sample, measures) • Further research (often arise from the limitations)

  36. QUANT. GENERAL

  37. Select topic Literature review Theoretical framework Research questions Theory and hypotheses Research methodology Conduct empirical data collection Analysis and results Discussion Conclusions (limitations and further research) Quantitative research process

  38. Design of a quantitative study • Define objectives, research questions and type of study • Research approach • Data collection methods (desk, field) • Sampling • Measurement and questionnaire design • Analysis methods • Timetables and costs • What can go wrong?

  39. Quantitative research process conceptualization concepts phenomenon operationalization Population definition variables measurement results sample Data matrix population analysis Data collection sampling

  40. Phenomenon, concept: company innovativeness Dimensions: New product introductions, ”generation” Implementation of new processes, ”adoption” Variables: (a) % of sales from products that were launched during the past three years, (b) how many new products were launched last year (a) investments on new manufacturing technologies during the past three years, (b) number of process improvements implemented last year Concepts and variables

  41. Operational indicator of a concept numeric Discrete or continuous Levels of measurement Nominal Ordinal Interval Ratio scale Variables

  42. Data matrix 5 variables 6 observations

  43. Types of data matrices • All have the same basic elements • variable j (k is the number of variables) COLUMN • Observation or case i (n is the number of cases) ROW • The value of variable j for case i (k x n is the number of values) CELL • But there are three types of k x n data matrices • Cross-sectional: the observations (rows) are independent • Time series: the observations (rows) are consequtive time periods with equal intervals • Panel: combination of cross-sectional and time series data. The cases are independent but the same variables are measured at several time periods, can be presented as wide or long

  44. Cross sectional data matrix

  45. Time series data matrix

  46. Wide panel data matrix

  47. Long panel data matrix

  48. Types of research • Exploratory, Descriptive, Explanatory, correlational, causal • Predictive, Optimization • Experimental, observational, ex post facto • Desk, field, laboratory, simulation • Cross-sectional, longitudinal, panel • Business vs. academic • Description usually not enough in thesis

  49. WHY (NOT) A QUANTITATIVE STUDY? • Philosophical background • positivism, empiricism, attempt to explain phenomena • objectivity, rationality, cumulative nature • hypotheses, deductive approach • If you cannot measure it, it isn’t there • ”Anglo-american” way of thinking about scientific research • Possibilities to get published (and cited) • Theory testing and theory development • no theory development without empirical testing • an empirical study is not scientific without a theoretical basis

  50. Theory is built from concepts and their relationships A researcher has to identify, define, and operationalize the concepts Deductive approach: concept – measurement – empirical results – feedback to theory Empirical studies are needed to test theories in varying contexts WHY (NOT) A QUANTITATIVE STUDY?