QUANTITATIVE METHODS IN IB RESEARCH

Kaisu Puumalainen

Lappeenranta University of TechnologyTel. 05- 621 7238, 040-541 9831

kaisu.puumalainen@lut.fi

INTRODUCTION

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

Timetable

30.8. introduction, research process, reporting 6.9. databases 13.9. research design 20.9. research design 27.9. international issues 4.10. analysis methods 10.10. assignment 1 DL 11.10. introduction to SAS 13.10. analysis with SAS 31.10. assignment 2 DL 15.11. exam 16.12. exam resit, if needed 11.5. 2011 exam resit if needed

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:1. Description and evaluation of proposal I2. Description and evaluation of proposal II3. 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 10.10.2010, return to kaisu.puumalainen@lut.fi

Review of the proposals

Overall structure, are all relevant issues covered?Problem specificationEmpirical 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 validityFormalities (references, writing, etc.)

Research proposal

1. Title2. Background3. The research problem and research objective(s)/question(s) (which further can be divided into

sub objectives/questions)4. 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?)5. Preliminary theoretical framework (What area(s) of business theory does the research topic

belong to.)6. Definitions (of special terminology used in the thesis)7. Limitations and scope (what issues will be excluded and for what reason)8. Method of research9. Structure of the research10. Tentative table of contents of the final report11. Available source material12. Tentative time table

http://www.des.emory.edu/mfp/proposal.html http://www.statpac.com/research-papers/research-proposal.htm

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

Evaluation of theses: scale

1 = weak2 = mediocre3 = satisfactory4 = good5 = excellent

Data collection and analysis exercise

DL 31.10.Graded 0-5, forms 25% of final gradePairworkData collection starts on 6.9. and more

detailed instructions will be given

REPORTING A QUANTITATIVE STUDY

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)

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 studyClearly articulate the study’s contributions

Literature search

Article databases– ABI, EBSCO, Elsevier, Emerald, JSTOR, Springer,

Wiley– http://www.lut.fi/fi/library/databases works through

VPNCitation information

– ISI Web of Science, ISI JCR– http://www.lut.fi/fi/library/databases works through

VPNGoogle Scholar

– http://scholar.google.fi/

Literature review

Stand-alone and embedded reviewsLiterature 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

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

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

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)

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

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.  

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.  

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.  

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.  

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

Example: measurement (1)

Dependent variables: international performanceWe 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).  

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). 

Example: measurement (3)

Independent variablesEntrepreneurial 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). 

Example: measurement (4)

Control variablesThere 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.

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)

example

Food Forest Chem. Metal Electronics Service ICT Total N of firms 20 21 18 79 23 17 39 217 % of firms 9 10 8 36 11 8 18 100 % international 55 85 83 75 91 53 35 68

Mean 1938 1957 1957 1967 1971 1953 1970 1962 Start year in industry S.D. 43.2 26.8 28.0 24.7 25.7 43.0 36.8 32.4

Mean 162.3 37.9 173.1 21.4 40.4 125.5 30.3 59.4 Sales M€ in 2003 S.D. 344.5 52.1 258.3 25.2 40.4 152.3 33.1 143.9

Mean 231.4 183.0 333.6 122.8 200.2 247.6 189.3 185.8 Employees in 2003 S.D. 224.6 175.1 254.4 75.8 167.3 224.3 132.2 166.6

example

Variable 1 2 3 4 5 6 7 8 9

1. Sales M€ .29b -.06 -.01 -.01 -.05 .02 .25b .25b

2. Intnl. experience -.19a .08 -.04 .02 .19a .34b .24b

3. Env. dynamism .18b .22b .03 .13 .02 .03

4. Entr. orientation .28b .19b .26b .04 .13

5. Rec. cap. number -.08 .03 -.01 .08

6. Rec. cap. success .21a .00 -.03

7. Intnl. performance .45b .24b

8. % of sales intnl .50b

9. # of countries

Minimum 2 1 1,14 2,33 0 1,86 2 0 0

Maximum 1177 204 6,57 6,06 7 5 9,43 100 140

Mean 59 28 4,14 4,14 3,98 3,57 5,91 52,05 12,23

Std. Deviation 144 25 0,99 0,74 2,14 0,61 1,68 32,6 17,61

Cronbach α n.a. n.a. .75 .74 n.a. .79 .91 n.a. n.a.

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

Reporting the results: testing

Varies by analysis methodModel fit statisticsTest statistic (+ standard error) and significance

level or confidence intervalMention that basic assumptions were checked for (Power of the tests) No software output as such Use tables!!

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.

Example: Std. regression coefficients of independent

variables Model fit

Dependent Model Env. dyn.

Size Intnl exp.

EO RC

number RC

succ. Adj. R2

R2 change

Base .09 .17 .31b .13b % of sales intnl Effect .09 .17 .31b .01 -.02 -.01 .11b .00

Base .08 .20a .20a .08b # of countries Effect .05 .20b .19b .10 .05 -.04 .07a .01

Base .17a .05 .21a .05a Intnl. perf., mean of items Effect .13 .07 .18a .21a -.03 .16 .10b .08a

Base .18a -.04 .24b .05a Satisfaction as a whole Effect .15 -.02 .21a .15 -.01 .22a .11b .08b

Base .18a .10 .11 .03 Capability development Effect .12 .12 .08 .23a .01 .16 .10b .09b

Base .20a .11 .12 .04a Image development Effect .18a .12 .09 .16 -.04 .11 .06a .04

Base .08 .10 .06 .00 Market access

Effect .04 .11 .03 .19a -.03 .09 .02 .05 Base .25b -.11 .22a .07b

Profitability Effect .22a -.10 .19a .14 -.04 .17a .11b .06a

Base .05 .12 .21a .07a Market share

Effect .02 .13 .18a .21a -.07 .08 .12b .05 Base .05 .04 .23a .03

Sales volume Effect .02 .05 .21a .12 .02 .11 .05 .03

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)

QUANT. GENERAL

Quantitative research process

1. Select topic2. Literature review3. Theoretical framework4. Research questions5. Theory and hypotheses6. Research methodology7. Conduct empirical data collection8. Analysis and results9. Discussion10. Conclusions (limitations and further research)

Design of a quantitative study

Define objectives, research questions and type of study

Research approachData collection methods (desk, field)SamplingMeasurement and questionnaire design Analysis methods Timetables and costsWhat can go wrong?

Quantitative research process

phenomenon

concepts

variables

population sample

Data matrix

results

conceptualization

operationalization

measurement

Population definition

sampling Data collection

analysis

Concepts and variables

Phenomenon, concept: company innovativeness

Dimensions:

(1) New product introductions, ”generation”

(2) Implementation of new processes, ”adoption”

Variables:

(3) (a) % of sales from products that were launched during the past three years, (b) how many new products were launched last year

(4) (a) investments on new manufacturing technologies during the past three years, (b) number of process improvements implemented last year

Variables

Operational indicator of a concept numeric Discrete or continuous Levels of measurement

– Nominal– Ordinal– Interval– Ratio scale

Data matrix 5 variables 6 observations

obs name sex age LikertA

1 Anne F 22 3

2 Berit F 15 4

3 Clas M 30 1

4 Daniel M 21 5

5 Emil M 35 2

6 Frida F 50 4

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

Cross sectional data matrix

obs Firm name

Industry Age Empl

1 Nokia Telec 50 60

2 Lukoil Ener 25 90

3 Valio Food 80 10

4 Shell Ener 45 100

5 GM Car 100 150

6 Motorola telec 30 20

Time series data matrix

obs Day Nokia OMX

1 1.1.2010 10.11 7900

2 2.1.2010 10.25 8000

3 3.1.2010 9.96 7550

4 4.1.2010 10.00 8011

5 5.1.2010 11.00 8321

6 8.1.2010 10.74 8205

Wide panel data matrix

obs Firm name

Emp 2008

Emp 2009

Emp 2010

1 Nokia 60 57 55

2 Lukoil 90 95 95

3 Valio 10 9 10

4 Shell 100 99 98

5 GM 150 130 110

6 Motorola 20 22 23

Long panel data matrix

obs Firm name

Year Emp

1 Nokia 2008 60

2 Nokia 2009 57

3 Nokia 2010 55

4 Lukoil 2008 90

5 Lukoil 2009 95

6 Lukoil 2010 95

Types of research

Exploratory, Descriptive, Explanatory, correlational, causal

Predictive, OptimizationExperimental, observational, ex post factoDesk, field, laboratory, simulationCross-sectional, longitudinal, panelBusiness vs. academicDescription usually not enough in thesis

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

WHY (NOT) A QUANTITATIVE STUDY?

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

Theory development and empirical research

1.  Conceptualization (innovativeness)

2.  Theoretical hypothesis= proposed relationship between concepts (innovativeness and cosmopoliteness are positively related)

3.   Empirical hypothesis= proposed relationship between operational measures of the concepts (early adoption of a product, travelling)

Theory development and empirical research

4. Analysis -> support or rejection of empirical hypothesis

5. Cumulative evidence from empirical studies -> generalizations, principles, laws

6. Theory develops or becomes more specific as cumulative empirical support is gained from varying contexts or anomalies are found

EXAMPLE: IDT

1. Empirical observations from several contexts: diffusion of an innovation has an S-shaped pattern

2. Theoretical explanation: it is a communication process within a social system

3. Adopters can be classified based on timing of adoption4. Theoretical hypothesis states a relationship between concepts:

cosmopoliteness has a positive effect on innovativeness 5. Empirical hypothesis states a relationship between the

operational indicators (measures) of the concepts: those who travel more outside the system, adopt the innovation earlier

6. Testing with data from different contexts (innovations, social systems) by different scholars strengthens the theory and reveals the limits -> replications are important

7. Extension of the theory to other levels of analysis: organization, country

HYPOTHESES

Act as a guide to research design and report Hypothesis vs. the null (H0) Testable? (eg. networks hard to measure, TCE) Simple? (2-3 concepts in one hypothesis) Exact? (has an effect /positive effect/ U-shaped

effect) Trivial? New? Well-reasoned? (analytical reasoning based on

theory + earlier empirical results) Descriptive or causal Max 5-10 hypotheses in an article Of which 1-2 are new hypotheses About 50% are supported by the data

Hypotheses, examples

There is a positive relationship between a firm’s export sales and the amount of R&D expenditures

Customer focus is a key driver of product quality in born global firms

In environments that are characterized by high market turbulence, TMT risk taking behavior does moderate the relationship between market orientation and performance

ROLES OF VARIABLES IN THE HYPOTHESES

Independent/predictor/explanatory/exogenous/cause variable/ x / IV

Dependent/criterion/endogenous/effect variable/ y /DV Moderating variable z / MoV

– “environment variable” or “contingency variable”– the relationship between x and y differs at different levels of z– Sharma et al (1981) Journal of Marketing Research 18(3):291-

300 Control variable /CV

– variable that is controlled for, not hypothesized but known to affect y

Mediating variable /MeV– The effect of x on y is mediated by MeV– Baron & Kenny (1986) Journal Of Personality and Social

Psych., 51, 1173-1182

PRESENTATION OF HYPOTHESES

IV DV

IV DV

MoV

IV DV

CV IV MeV DV

CAUSALITY x->y

x and y are correlatedx occurs before y the correlation between x and y is not spurious

(caused by some extraneous variable z)x and y can be observed indepedently from each

other (common method bias) the relationship can be explained /deduced from a

theory -> survey is not the best way to detect causality

TYPES OF CAUSALITY

Stimulus-response– A price increase results in fewer unit sales

Property-disposition – Company’s age and management’s attitudes about

exportingDisposition-behavior

– Job satisfaction and work outputProperty-behavior

– Social class and sports participation

Diffusion patterns (m, p, q)

Adoption year of country

Country’s wealth

Uncertainty avoidance

Individualism Power distance Masculinity Cultural distance from innovation center

CULTURAL EFFECT

TIME EFFECT

COUNTRY EFFECT

H1

H2a H2b H2c H2d

H2e

H3

H4

H5

Typical evolution of research

Identification of phenomenon X, conceptualization, dimensionality and measure development

consequences, so what? X -> Ydeterminants A -> XContextual dependencies and moderatorsE.g. market orientation

Task

You are working at the HR department of a large company. Your boss tells you that the IT department performs poorly due to its high employee turnover. He suggests that you should conduct a survey among other large companies to find out how they deal with problems due to employee turnover.

What are the hypotheses of your boss? What is the research problem? What would be your research questions and

hypotheses? What kinds of data matrices could you use?

Task:

The phenomenon of interest is Growth strategy of the firm

1. Which dimensions does this concept have?2. Which variables could be used for measuring the

dimensions?Write a hypothesis where growth strategy (or one of its

dimensions) is a Dependent variable Independent variable Moderating variable Dependent variable, but the effect is moderated by

another variable Mediator variable

SAMPLING

Sampling

Specify the population and informant(s)Specify what is to be measuredChoose the sampling frame Choose the sampling methodSpecify the sample sizeConduct the samplingCollect the data from the sampleAssess non-response bias

– Contact again, get the distribution of basic variables from another source and compare with the data, compare early and late respondents

Sampling

Population = group to which we wish to generalize the results

Census = collect data from whole population Larger samples yield more generalizable results,

smaller std errors, better power of testsSample must be representativeSample size n> 30, e.g. Finns n= 1000-2500Generalization from random samples

Population specification

Unit of analysis– Person, household– Team, SBU, firm, venture– Dyad, network– Industry, country

Basic characteristics (size, age,..)Must be relevant to the theoretical problem Informant(s) must have the ability and

willingness to respond

Sampling frame

= a list of units ín the population Statistics Finland and others Population Register Centre Telephone directories Kompass, Dun&Bradstreet Thomson, Amadeus, Ruslan Patentti- ja rekisterihallitus www Company databases Russian sampling frames?

Sampling methods

Random (or probability)– simple– systematic– clustered– stratified

non-random (non-probability)– convenience– snowball– judgement– quota

Random sampling

Systematic – Choose starting point randomly between 1-k, and take

every k:th– Sampling frame must be in no particular order

stratified– To ensure that subpopulations are adequately

represented– Determine strata and their shares of population– Sample proportionally (or not) from each strata

Random sampling

clustered– Divide the population into many small

clusters, and choose randomly which clusters are to be studied

– Within-cluster variation is desirable, but between-cluster is not

– Economical but not statistically efficientsequential

– Use various methods sequentially

Non-random sampling

Theoretically inferior, but sometimes practical

If statistical generalization is not requiredOk in exploratory researchConvenience or judgment samplingQuota sampling Snowball, when respondents are hard to

reach

Sample size

Can be determined if error margin is setMean & proportionError margin can be adjusted if sample >

5% of the populationlarger sample is needed when…

– More variation in the population– Smaller significance levels are required– More subgroups to be compared

nz

E

( )1 22

n

z

E 1

2

22

4

1

N

nN

Sample size

 min 30 cases per subgroup  min 5-10 per variable in multivariate analyses  larger sample yields better statistical power and

generalizability (see e.g. Cohen 1988 for power analysis)

 e.g. Finnish people -> 2000  not a given % of the population  usually 100-500 should be enough but do not forget that….

Sample size

 these apply for the real sample size, i.e. the usable responses you get

x= number of units taken from the sampling frame

.80*x will be contacted and eligible .80*(.80*x) will agree to participate .40*(.80*.80*x) will respond if you need 100 responses, x=100/.256=390

SECONDARY DATA

Design of a quantitative study: Data collection methods

Desk research– Company internal databases– Statistical databases– Commercial databases– Standard research products

• Consumer panels• Monitor, RISC, etc.

– Meta-analysis Field research

– Survey– Observation– Experiment

Research types & data collection

exploratory descriptive causal

Secondary sources

internal IS good ok

external databanks

good ok

services good ok ok

Primary sourcesqualitative good ok

survey ok good ok

experiment ok good

Desk research

advantages– Economical, fast– Suitable for studying the past – Longitudinal

limitations– Not specific to the research problem– Reliability?– Mostly directly observable simple indicators, no

measures for abstract constructs

Databases at LUT

Thomson One Banker, DataStream (global, financials of large companies)

SDC Platinum (global, M&A and alliances) ETLA company database (Finland, financials of top 600

companies) + Internet –database (Finland, statistics) Amadeus (Europe, financials & ownership of all

companies) Voitto Plus (financials of Finnish companies) ITU World Telecommunications /ICT Indicators (global,

country data) RISI (global, country and company data on pulp & paper) MarketLine (global, country data)

Free statistics on the web

– General statistics about countries:– http://www.undp.org/hdr2001/

http://globaledge.msu.edu/ibrd/ibrd.asp (very good!)

– www.ibrc.bschool.ukans.edu (very good!)–  www.GlobalBusinessWeb.com– http://faculty.insead.edu/parker/resume/person

al.htm (very good!)

– www.cia.org (world factbook)

Free statistics on the web

– Business magazines:– http://economist.com (financial)– www.businessweek.com (general)– www.ft-se.co.uk (Financial Times)– www.forbes.com (general)– www.pathfinder.com/fortune (Fortune) – www.wsj.com (Wall Street Journal)

Meta-analysis

Analyzes data from existing published quantitative empirical studies

Provides a synthesis of earlier studies by describing and explaining the means and variances of effect size across studies

What is the generalizability of findingsCan identify moderator effects Guidelines in Hunter & Schmidt (2004). Methods

of meta-analysis. Sage

SURVEY

Survey

The data is collected by asking the respondentsGood for measuring abstract conceptsE.g. Attitudes, values, opinions, intentions,

expectations, feelingsOk for measuring events that occured earlierThe respondent needs to cooperate with the

researcherThe most common method in business research

Response rate

Normally 10-95%– Depends on data collection method and procedure,

target population/ informant– Higher in interviews, internal company surveys– Aim at 30-40%, do not accept less than 15%

Effective response rate =Responses obtained / eligible sample size

The lower the response rate, the more you have to examine the possibility of non-response bias

Task: what is the response rate?

Your target population is Finnish exporting SMEs From the Amadeus database you find 45 000 firms

satisfying these criteria You take a random sample of 1 000 firms and phone them

– 50 cannot be reached at all – 40 are not SMEs any more– 200 are SMEs but not exporting– 60 are eligible but refuse to participate– You get back 200 questionnaires, of which 10 are returned empty

with a message saying that the firm has no exports Eligible sample size?

Motivators of response

Net individual benefit (appeals, personalization, incentives)

Societal outcome /norm (source, anonymity)Commitment /involvement (prenotification, DL,

follow-up)Novelty (envelope, cover letter, questionnaire)Convenience (postage paid)Expertise (informant choice)

Examples of appeals (Cavusgil & Elvey-Kirk,1998)

Social utility: Your assistance is needed! The information you provide can (1) contribute to an understanding of consumers’ views on auto care, and how they can be better served by retailers of maintenance service and supplies, as well as auto manufacturers, and (2) serve as inputs for auto repair legislation at state and federal levels. Your cooperation is truly appreciated.

Examples of appeals

Egoistic: Your opinions are important! It is very important for you to express your opinions so various retailers of maintenance services and supplies, as well as the auto manufacturers, will know the type of products and service facilities you would like to have available. Thanks for expressing your opinions.

Examples of appeals

Help the sponsor: We need your assistance! Your preferences and opinions are very important to our successful completion of this study. The accuracy of our findings depends wholly on the responses from each individual, like yourself, in the sample group. We truly appreciate your help.

Examples of appeals

Combined: Your opinions are important and useful! Your preferences and opinions are important for three reasons: (1) they can provide information that leads to an understanding of consumers’ views on auto care, as well as serving as inputs for auto repair legislation, (2) they can enable the retailers of maintenance services and supplies and aut manufacturers to know the types of products and service facilities you would like to have available, and (3) they will help us successfully complete this study. The accuracy of our findings depends wholly on the responses from each individual, like yourself, in the sample group. Thank you for your cooperation.

Task

Which of the previous appeals works best in the U.S.?

How about Russia or Finland?Which appeal works best in an academic /

commercial study?Which appeal works best in a sample of

consumers / professionals?

Type of appeal and response rateCavusgil & Elvey-Kirk,1998

Survey: data collection

Personal interviewTelephone interview Mail survey /fax/ e-mailWeb surveyOn-site terminal or questionnairesData collection methods are often

combined

Personal interview

+ Response rate+ Aids can be used+ Interviewer can ask

for more specific information

+ Flexible ordering of questions

+ Sampling frame not always necessary

+ Control over who responds

+ Can include a lot of questions

– Time-consuming– Expensive– Effect of the

interviewer on the responses

Telephone interview

+ Response rate+ Fast+ Interviewer can ask for

more specific information+ Flexible ordering of

questions+ Not very costly+ Control over who

responds+ Can be easily repeated+ Good for pre-notification

and follow-up

– No aids– Not many questions

(5-10 min.)– Easy and short

questions only– Representativeness

of the sample?– Effect of the

interviewer on the responses

Issues in interviews

Selection of interviewersBriefing of interviewersMotivating the interviewee Introduction of study

– Why me?– Why these questions?– How will the information be used?

Data collection– Coding of responses– How much to help the interviewee?

Mail or web survey

Design includes– Sampling frame– Cover letter– Questionnaire– Pre-testing– Return arrangements– Pre-notification – 2nd round– Incentives to solicit responses – follow-up

Mail survey

advantages– Fast for achieving large samples– Cost- efficient– Exact information, the respondent can take time to

find the answer– Impersonal, good for asking delicate issues

limitations– No control over who responds– Question ordering not very flexible– Length max 5-10 (20) pages– Does the respondent understand the question?– Low response rate, 10-50%

Web survey

design– With sampling frame or available to everyone– Accompanying message– Questionnaire + pre-test– Compatibility of data with analysis program– Incentives – E.g. SPSS Data Entry, Webropol

Web survey

advantages– Same as mail survey, but even cheaper and faster– Flexible ordering of questions– Elimination of inconsistent responses

limitations– Who responds– Are the population net users– Technical problems (different browsers, misclicks,

save without submitting and continue later)

Design of a quantitative study: Questionnaire design

What is to be asked (research framework!)– Is the question really needed– For what purpose /analysis

How to ask– Format of questions: open, closed, other,________– Direct or projective– Wording of questions

Order and layout of questionsPre-testing

Task: Which data collection method(s)?

A study targeted at people living in a new housing area. What kind of people are they? Why they moved into this area? Are they satisfied with the area?

A study targeted at LUT students. Which one of the three candidates are they going to vote for the president of the Student Union? Why?

A study targeted at those responsible for R&D in large companies in Finland. How do they protect their innovations? What kind of R&D cooperation do they have?

Cover letter

Personal, name of respondent Purpose of the research Importance of each response Confidentiality No right or wrong answers Incentives How long it takes to answer Instructions for returning (by which date, return

envelope) Contact information of researcher + signature Source of address (sampling frame) Thanks for responding

Wording of questions

Clarity and brevity Non-ambiguity No double-barreled questions

– Have you ever felt guilty for being unfaithful to your spouse?– Have you already stopped mugging your wife?

No leading questions Consistent use of pronouns (sinä/te) Behavior, attitude, opinion, intention Include negatively worded items (balanced scales) Variance!!! Response categories (exclusive, amount, order, open?)

Wording of questions

Did you happen to have murdered your wife? As you know, many people kill their wives nowadays.

Did you happen to have killed yours? Do you know about other people who have killed their

wives? How about yourself? Thank you for completing this survey, and by the way,

did you kill your wife? Three cards are attached to this survey. One says your

wife died of natural causes; one says you killed her; and the third says Other (explain). Please tear off the cards that do not apply, leaving the one that best describes your situation.

Order of questions

Easy ones firstLogic and headingsGeneral -> detailed Difficult and delicate ones near the endBasic background information first or lastOpen comments to the endThank you for your response

Scale types

Rating (evaluate each item separately)Ranking (compare to other items, pairwise

comparison, put in rank order, max 7 items)

CategorizationOpen ended

Response formats for rating scales

Likert- summated scale (usually 5 or 7-point, totally agree – neither agree or disagree - totally disagree)

Semantic differential, Osgood scale (anchored by opposite alternatives, good-bad)

Numerical scale (only anchors are labeled)Fixed sum (max 4-5 items, ipsative)Graphic (Visual Analogy Scale)

Task: what is wrong with the item?

Time is a limited resource All the senior executives of our company visit

regularly our most important customers A view exists, that all things are interrelated Self- fulfillment can be deduced from each person’s

place in a social process Contracts are unnecessary, because they are not

needed after they have been signed Innovativeness has a crucial impact on our

competitiveness When I evaluate my partner’s trustworthiness I pay

attention to open, fast and sufficient communication

Pre-testing

Representative of the real sampleSimilar situation (or personal interview)Ensure comprehensionEnsure varianceEnsure that questions can be answeredAre the respondents interestedHow long does it take to answer

Design of a quantitative study: Timetables

YEAR

Month1 Month 2 month 3 month 4 month 5 month 6 month 7responsibilityresearch problem specification COLideas of what to ask allformulation of questions:how to ask COLquestionnaire design COLcover page design COLcover letter /message design COLreminder letter /message design COLtranslation and back pretesting the questionnaire allmodifying the questionnaire allcopying the questionnaire allmailing arrangements allprenotifications allmailing allmailing the reminder allcoding the responses COLpreparing the data file COLanalysing data COLwriting the report All

RESEARCH SCHEDULE

Design of a quantitative study: Costs

Sampling frame Research assistant

– 1 week for preparing the sample– Can send about 10-20 questionnaires per day– Coding time 2-10 minutes per response– Data transformations, basic analysis and report 2-4 weeks

Mailing – Number of agreed participants *2– Reminders .8*the above

Copying, envelopes Translators Incentives Telephone costs Totals up to 15-20 000 €

Design of a quantitative study: What can go wrong? Research design

– Population specification– Selection bias– Sampling frame– measurement

Data collection– Question incorrectly presented– Coding – Interference during responding

Response errors– Intentional and unintentional (response styles ARS/DARS,

ExtremeRS, RRange, MidPointR) Non-response error

Problems with surveys

Leniency – Skewed distributions– Explanation of anchors may help– E.g. How important are the following factors…

Central tendency– Respondents tend to avoid margin alternatives,

especially if the topic is not familiar– Explanation of anchors, add scale points

Halo effect– Bias due to the respondent having a general attitude

towards the topic– Question order may help

Problems with surveys

Common method variance (bias)– Campbell & Fiske 1959– Correlation of two or more self-reported measures may be due to

the common source rather than true effect– Harman’s one factor test– Different respondents for different variables (if unit of analysis

e.g. Team)– Respond at different times

Consistency motif – Respondent has a lay theory and tends to confirm it– Reorder scales (x then y rather than y then x)

Social desirability– May cause the other problems mentioned above– Include scale by Crowne&Marlowe 1964, and partial out in

analysis

Task

A large corporation is sponsoring a study about sexual harrassment in the workplace. The research will be conducted because some female employees have expressed their concern about the issue.

What is the real purpose of the study? – Finding out the facts – Raising the employees’ awareness – Imposing change of behavior

How would you do the sampling? How would you collect the data? How would you minimize response and non-

response errors?

USEFUL ARTICLES

Determinants of Industrial Mail Survey Response: A Survey-on-Surveys Analysis of Researchers' and Managers' Views. By: Diamantopoulos, Adamantios; Schlegelmich, Bodo B.. Journal of Marketing Management, Aug96, Vol. 12 Issue 6, p505, 27p; (AN 5480001)

The effect of pretest method on error detection rates. By: Reynolds, Nina; Diamantopoulos, Adamantios. European Journal of Marketing, 1998, Vol. 32 Issue 5/6, p480, 19p, 5 charts; (AN 921930)

An Analysis of Response Bias in Executives' Self-Reports. By: Mathews, Brian P.; Diamantopoulos, Adamantios. Journal of Marketing Management, Nov95, Vol. 11 Issue 8, p835, 12p; (AN 4969428

Mail survey response behavior. By: Cavusgil, S. Tamer; Elvey-Kirk, Lisa A.. European Journal of Marketing, 1998, Vol. 32 Issue 11/12, p1165, 28p, 7 charts, 1 diagram; (AN 1401765)

Methodological Issues in Empirical Cross-cultural Research: A Survey of the Management Literature and a Framework. By: Cavusgil, S. Tamer; Das, Ajay. Management International Review (MIR), 1997 1st Quarter, Vol. 37 Issue 1, p71, 26p; (AN 12243002)

Response Styles in Marketing Research: A Cross-National Investigation. (cover story) By: Baumgartner, Hans; Steenkamp, Jan-Benedict E.M.. Journal of Marketing Research (JMR), May2001, Vol. 38 Issue 2, p143, 14p; (AN 4628360)

USEFUL ARTICLES

Armstrong, J.S. and T.S. Overton (1977) Estimating non-response bias in mail surveys. Journal of Marketing Research, 14 (3): 396-402.

Huber, George P. and Daniel J. Power (1985) Retrospective reports of strategic-level managers: Guidelines for increasing their accuracy. Strategic Management Journal, 6 (2): 171-180.

Podsakoff, P.M., & Organ, D.W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12, 531-544.

Reynolds, N.L., Simintiras, A.C., Diamantopoulos, A. (2003) Theoretical justification of sampling choices in international marketing research: key issues and guidelines for researchers. Journal of International Business Studies, 34 (1):80-89

Useful books

Ghauri, P., Gronhaug, K., Kristianslund, I. (1995) Research methods in business studies: A Practical guide. Prentice Hall, Englewood Cliffs.

Cooper, Schindler (2001) Business Research methods. Hair, Anderson, Tatham, Black (1998) Multivariate data analysis, 5th ed.

Upper Saddle River, NJ: Prentice Hall Cohen, J., Cohen, P., West, S.G. & Aiken, L.S. (2003), Applied Multiple

Regression/ Correlation Analysis for the Behavioral Sciences, (3rd ed.). Mahwah, NJ.:Lawrence Erlbaum Associates.

Cohen, J. (1988) Statistical Power analysis for the behavioral sciences, 2nd edn, Hillsdale: Lawrence Erlbaum Associates.

Aaker, Kumar, Day (2002) Marketing research Diamantopoulos & Schlegelmilch (1997) Taking the fear out of Data

analysis Hunter, Schmidt (2004) Methods of meta-analysis. Thousand Oaks: Sage Hofstede, Geert (2001) Culture’s Consequences: Comparing Values,

Behaviors, Institutions and Organizations Across Nations, 2nd edn, Thousand Oaks: Sage

MEASUREMENT

Measurement of constructs

If the concept is abstract, not readily observable or multi-faceted, a multi-item measure is always better than a single-item measure

Psychology good, management ok, marketing fair, strategic management and economics poor

TRUE Actual

Single-item Measure

TRUEActual-1Actual-2

Actual-3

Multi-item Measures

Cf. photographing an object from different angles

Two approaches to measurement

Reflective measurement– The latent construct causes variance on the observed indicators

(items)– The item is a function of the construct– If the construct changes, all the items change– The traditional and most common approach– 96% of constructs in top 4 mkt journals, only 69% should be

Formative measurement– The latent construct is a function of the indicators– If one of the items change, the construct changes– e.g. SES, HDI, country risk and other indices– (Diamantopoulos, Jarvis et al)

Assessment of validity and reliability differs

Scale development process

Definition of the concept to be measured Item generation Item reduction Data collection Item reduction Computing the scale Unidimensionality assessment Reliability assessment Validity assessment Generalizability assessment (replication, stability across

samples)

Concept definition

Literature review!Also look at other fields of study

/disciplinesThink about various points of view and units

of analysisOperationalization in earlier empirical

studies Qualitative field researchOwn work experience Distinguish from nearby concepts

Item generation

Earlier empirical studiesHandbooks of scalesQualitative methods (critical incident)Delphi, brainstorming, GDSS, etc.Focus groups, company interviewsAs many as possible, will be later reducedPositively and negatively wordedClear and unequivocalMin 10 per concept

Butler 1991 operationalization of antecedents of trust

An inductive approach

1. managers described a person they trust and another they do not trust

2. They described critical indicents that led to the emergence or loss of trust

3. altogether 280 + 174 antecedents were found

4. They were classified by students into 10 groups

5. A definition was written for each group

6. 4 items were generated for each group

Item reduction

Expert opinionGrouping

– The concept definitions are presented to the experts and they combine each item with the corresponding concept

Only those items that experts agree on, are retained

Pilot study / pre-test sample– Distribution of each item– Inter- item correlations (min .30)– Exploratory factor analysis

Computing the measure

Sum of items (SPSS: compute, sum)Mean of items (SPSS:compute, mean)

– Generally better than the sum, you may want to compare scales with different number of items

Factor scoreWeighted mean of items (weights from the

factor loadings)

Unidimensionality

Factor analysis– exploratory– confirmatory– remove items that load less than .40 or have

high loadings on wrong dimensions – split-sample validation of the factor structure– see article by Gerbing and Anderson (JMR)

Reliability and validity of scalesScale

Evaluation

Reliability Validity

Test-RetestInternal

ConsistencyAlternative Forms Construct

Criterion

Content

Convergent Validity

Discriminant Validity Nomological

Validity

Reliability

Absence of random error types:

– Stability (“test-retest reliability”)– Equivalence (“parallel forms reliability”)– Consistency (“split-half reliability”)– Homogeneity (“internal consistency

reliability”)– Inter-rater reliability (concordance)

Reliability

Cronbach alpha– Measures the internal consistency of a scale– More items -> higher alpha– Is based on inter-item correlations (min .30)– Alpha should exceed 0.60 in exploratory

research, 0.70 in theory testing (Nunnally)– Remove items with item-total correlation less

than .50

Cronbach alpha

N of items

Average inter-item correlation Alpha

2 0,3 0,4615382 0,5 0,6666672 0,7 0,8235293 0,3 0,56253 0,5 0,753 0,7 0,8755 0,3 0,6818185 0,5 0,8333335 0,7 0,9210537 0,3 0,759 0,2 0,692308

rN

rN

*)1(1(

*

N=number of items

r= average inter-item correlation

Validity

a) External v of findings = generalizabilityb) Internal v of findings = if x actually causes yc) V of measurement scalesAre we measuring what we purport to measureAbsence of systematic error (bias) in

measurement– Content / face validity– Criterion validity (predictive validity)– Construct validity (convergent, discriminant,

nomological)

Content validity

Can the measure yield answers to the research problem

Can the measure capture the domain of the construct

No matemathical methods to assessAlso known as face validityAssessment based on quality of concept

definition and content of the items

Criterion validity

Do the measures provide a good model fit or a good predictive accuracy

Concurrent or predictive validity Is the criterion itself measured in a valid way

– relevance (e.g. performance)– unbiased– reliable (stability)– availability

Construct validity

Is the measure theoretically valid captures the whole concept but nothing but the concept

(deficiency, contamination) convergent validity (yields similar results as other

measures of the same construct)– correlation, MTMM

discriminant validity (differs from other constructs)– Factor analysis, MTMM

nomological validity (is related to other constructs as predicted by theory)

Multitrait - Multimethod Matrix (Campbell & Fiske, 1959)

Method 1 Method 2 Trait a Trait b Trait a Trait b

Trait a b1 Method 1

Trait b m1 b1 Trait a va d b2

Method 2 Trait b d vb m2 b2

b1 = reliability for method 1va = convergent validity for both methods wrt trait am1 = discriminant validity for method 1d = “nonsense”-correlation

Requirements: • v > 0 and "high enough"• v > d• v > m• d low

Correlationcoefficients{

ExampleMosher Forced Choice Guilt Scale

3 traits– Guilt feelings about sex– Hostile guilt– Guilt concerning morality

3 methods– Incomplete sentences "When I dream about sex …"– Forced choice

• " When I dream about sex …"a) I don't remember a thing in the morningb) I feel happy when I get up

– True/False• "When I dream about sex I wake up feeling happy"

MTMM matrix for the Mosher Forced Choice Guilt Scale

TF(true/false)

FC(forced choice)

IS(incompl. sent.)

SG HG MC SG HG MC SG HG MCSG .91HG .52 .84TFMC .68 .50 .84SG .86 .56 .73 .97HG .53 .83 .53 .61 .96FCMC .63 .54 .83 .70 .58 .92SG .78 .51 .63 .79 .54 .57 .72HG .24 .67 .23 .33 .73 .37 .32 .65ISMC .47 .40 .66 .48 .49 .70 .49 .28 .55

Sexual

Hostile

Morality

FC very reliable, TF too, IS not

Good convergent

validity

Discriminant validity OK

Generalizability assessment

The performance of a measure should always be evaluated in a separate sample

Replications help to set the limits to the applicability of theories in different contexts

Cross-cultural validationLISREL group comparisons

Task: market orientation

Each group will receive a concept definition and scales for market orientation– A: Kohli, Jaworski & Kumar (1993): MARKOR- a measure of

market orientation, JMR, 30 (4):467-477– B: Narver & Slater (1990): The effect of a market orientation on

business profitability, JM, 54 (4):20-35 Read it and discuss:

– Content validity– Clarity of the items– Overlap of the items– Use of reverse coded items– Generalizability across contexts

Books on measurement

http://www.socialsciencesweb.com/ a lot of books there! Nunnally & Bernstein (1994) Psychometric Theory. McGraw Hill DeVellis (1991) Scale Development: Theory and Applications. Sage Marketing Scales Handbook: A Compilation of Multi-Item Measures, Vol. I-III

Authors: G. Bruner , K. James , P. Hensel Handbook of Marketing Scales: Multi-Item Measures for Marketing for Marketing

and Consumer Behavior Research by W.O. Bearden, R.G. Netemeyer Measures of Personality and Social Psychological Attitudes : Volume 1: Measures

of Social Psychological Attitudes. Authors: J. Robinson , P. Shaver , L. Wrightsman

Metsämuuronen (2004): Tutkimuksen tekemisen perusteet ihmistieteissä Price JL and Mueller CW. (1986). Handbook of organizational measurement.

Marshfield,Mass.: Pitman. Rubin RB, Palmgreen P & Sypher HE. (1994). Communication research

measures: A sourcebook. New York: Guilford Pr. Psychoogy measures: http://www.ull.ac.uk/subjects/guides/psycscales.shtml

Articles on measurement

Churchill (1979) A paradigm for developing better measures of marketing constructs. J Mark Res, 16(1):64-73

Campbell et al (1973) The development and evaluation of behaviorally based rating scales. J Appl Psych, 57:15-22

Mullen (1995) Diagnosing measurement equivalence in cross-national research. J Int Bus Stud, 26(3):573-96

Campbell & Fiske (1959) Convergent and discriminant validity by the multitrait-multimethod matrix. Psych Bulletin 56(March):81-105

Gerbing & Anderson (1988) An updated paradigm for scale development incorporating unidimensionality and its assessment. J Mktng Res 25(May):186-192

Hinkin (1995) A review of scale development practices in the study of organizations. Journal of management, 21(5)

Jarvis, Mackenzie & Podsakoff (2003) A Critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30 (Sep):199-218

Boyd, Gove & Hitt (2004) Construct measurement in strategic management research: illusion or reality. Strategic Management Journal

Diamantopoulos & Winklhofer (2001) Index construction with formative indicators: an alternative to scale development, Journal of Marketing Reseach, 38(May):269-277

Cbu part II

OBSERVATION&EXPERIMENT

Field research

Survey– Personal interview /CAPI– Telephone interview /CATI– Mail survey /fax– Web survey /e-mail

observationexperiment

Observation

Nonbehavioral– Historical or financial records (=secondary data)– Physical condition analysis like store audits– Process or activity analysis like traffic flows

Behavioral– Nonverbal like movements– Linguistic– Extralinguistic (loudness, rate, interruption..)– Spatial

Observation

Real time information on overt behavior or environment

Must be easily codable In a natural environmentShould the object know? (Hawthorne)Should the observer participate? If the purpose of the study needs to be disguised

(e.g. Phantom shoppers in service quality studies)

Experiments

True and field experiment Good for detecting causality The researcher manipulates the independent variable Test group and control group Blind and double-blind treatment Easy to replicate Hard to generalize from Best for easily measurable concepts Ethics of manipulation? (plasebo-knee surgery)

Phases of experiment

Selection of variables Decide how to manipulate the treatment levels Controlling the experiment environment Design of the experiment Selection of subjects and assignment to experiment and

control groups (random or matched) Pilot experiment, revision, experiment Analysis

Experiment designs

pre-experiment (statistically weak)– X-O– O-X-O– Test group X-O and control group O (non-random assignment)

true experiment (random or matched assignment)– Test group O-X-O and control group O-O– Test group X-O and control group O– Many test groups, O-X-O, but each group has a different level of

X – Randomized block, Latin square, factorial design

field experiment, quasi- experiment– Assignment to groups non-controllable

Validity of experiment

Internal validity (is there really causality)– O-X-O other factors that may cause a change in O– Changes in the subject– Subject learns from the first measurement– Researcher or measurement instrument changes– Assignment to groups, stability of groups– Extremes tend towards the mean

External validity (generalizability)– Voluntary subjects

ANALYSIS

Analysis

Preliminary examination and classification of open ended responses

Coding and inputtransformations description, checking of normalityTesting the hypothesesDiscussion and conclusionsSoftware: Excel, SPSS, SAS, Statgraphics,

DataFit, E-Views, Stata, etc.

Coding

Numerical variables if at all possibleExact first, you can classify laterDefine informative variable and value

labelsWhat to do with missing data (NA)What is a missing value (checklists)Identification variables (ID number, dates,

interviewer, etc.)

Transformations

Classification of open-ended responsesClassifying continuous variablesReversing itemsComputing multi-item scalesComputing lags, logs or other new variablesChecking for inconsistent responses Removing outliers?

Hypothesis testing

Univariate or bivariate tests based on measurement level and normality of distribution

5% significance level normallyRemember also practical significanceYou hope to reject the H0 -> support for your

research hypothesisTests of means and independenceCorrelationsMultivariate analysis

MULTIVARIATE ANALYSIS

Phase of research

dependent independents

Reliab,val concepts na Na

FA concepts na Na

CA typologies na Na

LinReg effects continuous continuous

ANOVA effects continuous categorical

LogReg effects categorical continuous

Factor analysis

Interdependence of continuous variables Reduce variables, detect underlying dimensions Used in measure development Cavusgil, S.T. (1985) Factor congruency analysis..Journal

of the market research society, 27(2):147-155 Report:

– Extraction method (PC, PAF, ML)– (rotated) factor loadings– Communalities– Eigenvalues + % of variance explained– KMO and Bartlett’s test– How the number of factors was chosen

Regression analysis

The most common method of hypotheses testing Dependent variable continuous Independent variables continuous or dummies Can incorporate interactions, mediating or moderating effects Report:

– Model fit (R square and significance, increase in R square if hierarchical model)

– Regression coefficients (beta), std. errors or t-values, significance– Estimation method– Violations of assumptions (residual analyses, multicollinearity,

influence statistics)– Validation

Discriminant analysis and logistic regression

Dependent variable nominal, a priori groups Independent variables continuous or dummy How the independents can separate between the

groups: understanding or prediction LR less sensitive to violation of assumptions Report:

– model fit (Wilks lambda, pseudo R squares)– Effects and significance of independents (DF

coefficients or loadings, exp(B))– Classification results (hit ratio)– Validation

Analysis of variance

Oneway ANOVA, ANCOVA, MANOVA, MANCOVA

Continuous dependent variable /variate Independent variables can be factors (nominal)

or covariates (continuous) Interactions among the independents can be

modeled Suitable for testing hypotheses Report: estimated group means and the

significance of each effect, + the full model, post hoc differences or contrasts

Cluster analysis

Grouping the cases into homogeneous subsets Grouping is based on several variables Not for hypotheses testing Report:

– proximity measure, – clustering algorithm, – clustering method, – criteria used for selecting the number of clusters,– cluster description: centroids, n of cases– validation

Structural equation modelsLISREL

Confirmatory analysis Multiple interrelated dependence

relationships simultaneously Accounts for measurement error Can incorporate latent variablesPath models, group comparisons,

moderating effects, etc..Other programs: AMOS, EQS

Fit criteria in LISREL

Chi square (should not be significant)

Goodness of fit-index GFI, AGFI (>.90)

Incremental fit NFI (>.90)Residual statistics RMSEA, RMSR

(<.08)Critical N

IB ISSUES

IB challenges and issues

Majority of the studies in IB are– Ethnocentric /replications (Adler, 1983) – Static, cross-sectional surveys– Manufacturing sector– Micro-level unit of analysis– Single informant– Judgement sampling

Equivalence– Sample (why these countries?)– Construct (”urban” or ”soft drink”)– Instrument (back-translation, Osgood is internationally most

consistent response-format)– Data collection

A review of research methodologies in IB, Yang et al. (2006) IBR, 15:601-617

All empirical studies from JIBS, MIR, JWB, IMR, JIM, IBR 1992-2003

1296 studies, 67.3% of all articles were empirical60% surveys, 33% secondary data, 2%

experiment61% one country, 17% two countries, 22% more

countries89% Europe, 66% Asia, 52% North America, 2%

Africa39% USA, 16% UK, 14% Japan, 11% China

A review of research methodologies in IB, Yang et al. (2006) IBR, 15:601-617

50% managers/CEOs, 11% consumers, 10% financial data, 10% government data. 4% students

Median sample size around 200Mean response rate in mail surveys 27%Very few studies using multiple informants

European data collection(source:ESOMAR, 1990)

FRA NED SWE SUI UKMail 4 33 23 8 9Phone 15 18 44 21 16Street 52 37 - - -Home/office - - 8 44 54Groups 13 - 5 6 11In-depth 12 12 2 8 -Secondary 4 - 4 8 -Other 0 0 14 5 10

ADLER’S TYPOLOGY

Parochial (single culture, assumes universality, 80% in 1970-80)

Ethnocentric (second culture replications, questions universality, standardized research design, often interprets differences as design defects)

Polycentric (many individual domestic studies, denies universality, mostly inductive, anthropology)

Comparative (many cultures but none dominant, looks for universality and culture specificity of elements)

Geocentric (MNOs, search for similarity across cultures) Synergistic (intercultural interaction, action research)

Dilemmas in comparative research

What is culture?Can country be used as a surrogate for it?Is culture x or y or contingency variable?Does cultural perspective of the researcher

affect the interpretation of findings?Identical topic vs. equivalent research

design?

Dilemmas in comparative research

Topic should be– Conceptually equivalent– Equally important and appropriate

Size of sample (cultures & within) Representative or matched samples Translation and back to ensure equivalence in meaning Scaling procedures equivalent, similar pattern of

correlations Administration (interviewer, data collection, timing)

Laurent (1983) The cultural diversity of western conceptions of management. Intnl studies of mgmt

and organization, 13(1-2):75-96

It is important for a manager to have at hand precise answers to most of the questions that his subordinates may raise about their work

0

10

2030

40

50

6070

80

90

% o

f m

an

ag

ers

ag

ree

Problem-solvers – experts – loss of face

Chinese described an ideal picture (not real) and kept the questionnaires

Methodological problems in cross-cultural research (Nasif et al.) Criterion problem

– Definition of culture– Country as a surrogate for culture– When is culture a contingency?– Cultural biases of researchers– Cultural biases of national theories

Methodological simplicity– Difficulties of rigorous designs– Cross-sectional case studies– One-shot static studies– Static group comparisons– Functional equivalence– Time problems– Single-discipline studies– No synergy

Methodological problems in cross-cultural research (Nasif et al.)Sampling issues

– Selection of cultures and subjects (convenience)– Student samples– Sample size and representativeness– Matched samples– Independence of samples (Galton’s problem)– Description of the characteristics of the samples

Instrumentation– Equivalence of language (transalation)– Equivalence of variables– Equivalence of scaling (response formats, PRC,JPN

central tendency unless even number of alternatives)

Opinions(Kumar, 2000)

Four men, a Saudi, a Russian, A North Korean, and a New Yorker are walking down the street. A researcher says to them: ”Excuse me, what is your opinion on the meat shortage?”

The Saudi says: ”What’s a shortage?”

The Russian says: ”What’s meat?”

The Korean says: ”What’s an opinion?” and

the New Yorker says: ”What’s excuse me?”

Methodological problems in cross-cultural research (Nasif et al.) Data collection

– Equivalence of administration– Respose equivalence– Timing of data collection– Status and other psychological issues– Cross-sectional versus longitudinal data collection

Data analysis– Qualitative vs. quantitative data– Non-parametric vs. parametric statistics– Univariate vs. multivariate analyses

Level of analysis– Data collection and analysis at one level, inferences at another– Individual/organizational/societal– Ecological fallacy (Hofstede) or aggregation problem

Sampling choices (Reynolds et al. (2003) JIBS, Vol.34(1):80-89)

Type of research

Objective Sampling objective Sample attributes Sampling method

Descriptive Examine attitudes and behavior within specific countries

Within-country representativeness

Estimate sampling error

Random within each country

Contextual Examine attributes of a cross-national group

Representativeness of the cross-national population

Estimate sampling error

Random within the population

Comparative Examine similarities or differences between countries

Cross-national comparability

Homogeneity to control for extraneous factors

Non-random acceptable, matched

Theoretical Examine the cross-national generalizability of a theory or model

Cross-national comparability

Homogeneity or deliberate heterogeneity

Non-random acceptable

Operationalizing culture

Hofstede’s dimensions (Culture’s consequences, 1980, 2001)– Individualism / collectivism– Power distance– Masculinity / femininity– Uncertainty avoidance– (Long-term orientation)

High vs. low context cultures (Hall, 1959) Cultural orientations (Kluckhohn & Strodtbeck, 1961) Individualism – collectivism (Triandis, 1983)

Hofstede scores

Country PDI UAI IDV MAS

Finland 33 59 63 26

Russia 93 95 39 36

Germany 35 65 67 66USA 40 46 91 62

Japan 54 92 46 95

Denmark 18 23 74 16http://spectrum.troy.edu/~vorism/hofstede.htm

Task: Implications for data collection

Discuss how the differences in cultural dimensions may affect data collection, e.g.– Choice of data collection method– Choice of informants– Choice of objective vs. subjective measures– Choice of direct vs. projective measurement– Use of appeals to solicit responses

Articles on IB challenges and issues

Adler (1983) A typology of management studies involving culture, JIBS, 14(2):29-47

Adler et al. (1989) In search of appropriate methodology…JIBS, 20:61-74.

Buckley & Chapman (1996) Theory and method in IB research. IBR, 5(3):233-245

Cavusgil & Das (1997) Methodological issues in empirical cross-cultural research…MIR, 37(1):71-96

Nasif et al. (1991) Methodological problems in cross-cultural research…MIR, 31(1):79-91

Coviello & Jones (2004) methodological issues in international entrepreneurship research. JBV, 19:485-508

Kumar (2000) International marketing research. Book