Doing Quantitative Research 26E02900, 6 ECTS Cr.

Doing Quantitative Research26E02900, 6 ECTS Cr. Olli-Pekka Kauppila Daria Volchek

Doingquantitativeresearch Different to statistics courses, our approach is very hands-on and focused on carrying out a quantitative research project in management studies After this course you should be prepared to design, execute, and report a research project using basic quantitative methods In addition, you should be able to understand and evaluate more advanced statistical methods that are most commonly used in management studies Our primarily focus is on the use of survey methods

Today’slecture AM session • Whathappensbeforewecollect the data? PM session • Whatdowedofirstaftercollecting the data

Learning objectives – AM session Gain a deeper understanding of what research question is and how is guides the research process Improve skills to develop testable hypotheses to address the research question(s) Develop ability to evaluate different data sources and choose an appropriate sample to test your hypotheses Deepen knowledge of different forms of measures in management studies Understand various aspects of different data collection methods Understand and appreciate the ethics of quantitative research

Learning objectives – AM session “Theory part” - i.e. what is it that you are set out to investigate “Empirical part” - i.e. how would you test your predictions empirically “Ethical part” - i.e. what are the rules and standards when studying the topic Gain a deeper understanding of what research question is and how is guides the research process Improve skills to develop testable hypotheses to address the research question(s) Develop ability to evaluate different data sources and choose an appropriate sample for different research projects Deepen knowledge of different forms of measures in management studies Understand various aspects of different data collection methods Understand and appreciate the ethics of quantitative research

Part 1:Research question and hypotheses

Research process How and when to measure the variables? What might be the answer? → Hypotheses & theoretical model Where and how to collect data? Research problem / question Testing the hypotheses Reporting the findings Answering the research question Evaluation the quality of data and measures

Research question Explicates the purpose of your research • However, it is not always stated explicitly Good research question: • Addresses an important topic - i.e. is interesting • Current knowledge does not have an answering to it, OR • Current knowledge provides an incorrect or insufficient answer • Answering the question is likely to change the way we think about the topic • Clear, focused, empirically addressable PhD Students: Note that you need to provide a theoretical contribution! – i.e. it is not sufficient to demonstrate how a pre-existing theory works in an empirical setting

Examples of researchquestions What factors determine the likelihood of an organization's settlement on a new, contested institutional practice? Why do some firms pay more attention to stakeholder demands than others? Given that individuals have an overwhelming desire to gain status and they receive significant advantages when they have it, how do they react when they lose it?

Classroom exercise I Based on your reading of the article introduction, please answer the following three questions: What is the research question in this article? How does the author justify the importance of addressing this particular question? To what theories the author claim that his findings contribute and how?

Forming the hypotheses The primary purpose of a theory section is to ground hypotheses The role of hypotheses is twofold: • Identify and organize different issues that we need to consider in order to answer the research question • Provide predictions of how different elements in the model are linked together The argumentation preceding each hypothesis • Positions your hypothesis in relation to related research • Develops a clear and logical argument explaining why the hypothesized relationship is expected

Pitfalls to avoid • Stating the obvious • Lack of coherence • Fragmented theorizing • Stretching theories too far • Empirically untestable hypotheses

Grounding hypotheses to address the research question Research question: Given that individuals have an overwhelming desire to gain status and they receive significant advantages when they have it, how do they react when they lose it? → Guidancefromtheory (hmm…howcoulditbe?) → Argumentation→ Hypothesis 1. The negative effect of status loss on performance quality for high-status individuals is stronger than it is for low-status individuals. Hypothesis 2. Self-affirmation moderates the effect of initial status position on performance quality after status loss: high- (but not low-) status individuals perform better when they are given an opportunity for self-affirmation.

Part 2:Data collection, data sources, and measures

Research process How and when to measure the variables? What might be the answer? → Hypotheses & theoretical model Where and how to collect data? Research problem / question Testing the hypotheses Reporting the findings Answering the research question Evaluation the quality of data and measures

Objective data sources Independent of human perception The word “objective” does not necessary mean that the measure is valid or reliable Examples of objective measures used in management studies • Number and types of the firm’s alliance partners • Firm performance data from the annual report • Blood pressure, cortisol activity, cognitive ability • Employee performance in terms of sales volume etc. • Age, organizational tenure, functional affiliation, salary…

Subjective data collectionsources Based on human perception (i.e. thing that we ask individuals to assess) Self-reported • What are the issues that individuals can most reliably report themselves? • E.g. Emotions and preferences that others are not aware of Other-reported • When is it better that someone else is evaluating the focal individual? • E.g. Task performance that individuals would be likely to report in a biased manner

Choosing a sample I.e. drawing a research sample from population Must be appropriate for the research question(s) • E.g. if you are studying work-related phenomena, it is appropriate to sample individuals in work contexts • Various sampling methods exists (there is a good summary in Wikipedia) • Probability sampling: each member of the population has a (equal) chance to be sampled; e.g. when you study Finnish SMEs and randomly draw a sample from the entire population • Non-probability sampling: a sample is drawn non-randomly; e.g. employees of a certain firm (convenience sample). When using non-probabilistic methods, you must carefully consider the generalizability of your findings

Samplesize The higher the number of observations (e.g. number of respondents), the higher the likelihood that you find significant relationships In most research designs in management studies, the required sample size varies between 100 and 300

Validity A measure is valid when it accurately represents what it is supposed to E.g. which one is a more valid measure of employee satisfaction: (I) employee’s self-report of his or her job satisfaction, or (II) the number of days the employee is absent from work? To assure validity, make sure that the operationalization of the measure is entirely consistent with the theoretical definition of the construct that you are measuring

Reliability A measure is reliable when it produces consistent results E.g. Which of the following items you think could be used to form a reliable measure for job satisfaction? • This organization has a great deal of personal meaning for me • Most days I am enthusiastic about my job • I want to learn as much as possible from my job • I feel fairly well satisfied with my present job • In my job, I prefer tasks that really challenge me so I can learn new things • I consider my job rather unpleasant • I find real enjoyment in my work • I desire to completely master my job • I do not feel a strong sense of "belonging" to my organization

Reliability A measure is reliablewhenitproducesconsistentresults E.g. Which of the followingitemsyouthinkcouldbeused to form a reliablemeasure for jobsatisfaction? • This organization has a great deal of personal meaning for me • Most days I am enthusiastic about my job • I want to learn as much as possible from my job • I feel fairly well satisfied with my present job • In my job, I prefer tasks that really challenge me so I can learn new things • I consider my job rather unpleasant • I find real enjoyment in my work • I desire to completely master my job • I do not feel a strong sense of "belonging" to my organization

Twotypes of variables • Study variables • Dependent variables (outcomes that we are trying to explain) • Independent variables (what we argue explains the outcome) • Control variables (rival explanations for the outcome) • Control variables are any variables that may offer alternative explanations of the outcome • All the most likely explanations of dependent variable should be controlled for (especially if they are likely to ) • It is important that your independent variables explain a proportion of variance in dependent variable that is above and beyond the variance that is explained by control variables • E.g. What control variables you would include in investigating the effect of organizational commitment on individual creativity?

Operationalization of measures Variables are always numerical, and in many cases, we must transform qualities into numerical form • Nominal scale: dummy variables (two categories); e.g. 1 = condition (e.g. female) is met and 0 = condition is not met • Ordinal scale: variables with values presented in rank order (e.g. high-low); e.g. 7 is the highest value - 1 is the lowest value • Ratio scale: scales with an interpretative zero point; e.g. firms’ net sales in euros In management studies, we are often interested in things that are not directly observable • Capabilities, orientations, emotions, attitudes, behaviors, etc. • Thus, we use multi-item scales to better establish reliability

Likert scales Uncaptured variable Variablecaptured with 6 items Usually, 5-point or 7-point scales Midpoint of the scale (usually 3 or 4) is defined as ”neutral” (i.e. equally characterized by both endpoints of the scale) Each item (i.e. survey question) captures a key aspect of the variable

Job satisfaction on a questionnaire

Classroom exercise II In your research project, you are interested in studying how team leaders’ inspiration leadership influences team performance Inspirational leadership = leadership style that “focuses on communicating a compelling vision for the team, expressing confidence in team members, and energizing the team” Based on your extensive literature review, you have learned that there are no pre-existing measures capturing inspirational leadership Thus, you decide to develop a new scale • Come up with measure items that you think would capture inspirational leadership (validly and reliably) • What is your data source (i.e. who do you ask) and why?

Oops, actually we found a pre-existing measure after all… is it similar to yours? Inspirational leadership scale (based on Bass, 1985): • “My leader makes everyone in the team enthusiastic about the team’s assignments” • “My leader encourages me to express my ideas and opinions” • “My leader has a sense of mission that he/she transmits to me” • “My leader is an inspiration to me” • “My leader excites us with his/her visions of what we may accomplish if we work together as a team” • “My leader makes us believe we can overcome anything if we work together as a team.”

Using cross-sectional data and data with timelags When your hypotheses suggest that there is some type of change or causality between the variables, it is important that there is a time lag between independent and dependent variables • Cross-sectional data shows that there is correlation, but does not suffice to establish causality inferences • E.g. collect data on “inspirational leadership” at Time 1 and data on “team performance” on Time 2 (to determine the length of time lag between 1 and 2, you need to estimate the effect time) • Doctoral students: the use of cross-sectional data is one of the most common reasons for rejection at top journals

Common methodvariance When common method bias is present, the measurement method is the reason for significant relationships between the study variables Is usually caused by a single individual being the source of both independent and dependent variables The most relevant bias related to research design To control for common method variance • Do not use cross-sectional designs • Whenever possible, obtain data from different sources (also, mix subjective with objective data) • Protect respondents’ anonymity • Avoid ambiguous questions

Ethics in research and publishing Based on the Code of Ethics of the Academy of Management (Academy of Management Journal 2011, Vol. 54, No. 6, 1299–1306.) All researchers have an obligation to be familiar with general ethical principles – not knowing is not an acceptable excuse

Some essential ethical guidelines (not a complete list!) Always explicitly identify, credit, and reference the author of any data or material taken from written work, whether that work is published, unpublished, or electronically available – i.e. no plagiarism of any kind Keep your promises to the research subjects Never cause any harm to your research subjects Never fabricate data or falsify results Do not omit data or findings for the sake of presenting “better” results Once the results are published, they are published. Do not try to publish the same (or closely related) results again

Doing Quantitative Research 26E02900, 6 ECTS Cr.