Research MethodologyChapter IWhat is research? Research is NOT just collecting facts, the way you do your research is important. Analyze your research in a strict and systematic way. Need to have a clear purpose to do a research, you need to find something out from this research? Research IS collecting date & interprets in a systematic way! It is not only base on intuition and gut feelings. If you do the research it is for a specific purpose, you have the motivate why you are doing this in a logic way. Activia example: is this advertising research? This is not the type of research that we are going to study! Research is not trying to confirm what you are trying to thoughResearch is something that people undertake in order to find out things in a systematic way, thereby increasing their knowledge.

Business and management researchOften the problem that youre dealing with is not in one discipline. Sometimes you need the knowledge and discipline from others, to make your research a success. Theory and practice? Is theory or practice necessary to do a good research? Yes, you need that to apply in your research! Not separate but can influence each other; theory can apply in practical cases and vice versa. In the best system there are not separate aspects BUT they influenced each other.Fundamentals research Vs. Applied research

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PURPOSE Expand knowledge of processes Universal principles Findings of significance and value to society in general

PUROPOSE Improve understanding of particular problem Results in solution to problem New knowledge limited to problem Findings of practical relevance

CONTEXT Universities Choice determined by researcher Flexible time scalesCONTEXT Organizations and universities Negotiation with originator Tight time scales

In sum, research (also business and management research) should Collect data systematically Interpret data systematicallyDifferent types of Have a clear purpose: to find things outresearch depending on its purpose and context

Examples: Research team investigate the idea of a new supermarket Why applied research? It is for a commercial purpose! MEAN problem is that a supermarket is trying to open a new store.

Web address on TV advertisementApplied or fundamental?Fundamental

Fundamental research because student can try to see if people see the website on a commercial TV

Rise of the internetFundamental or appliedApplied

It is more a global research You want to understand the general process,

Jewelry set of a specific brand

Fundamental or applied?Applied

Because it is a specific brand Consultant research more for one company

Wherever your research projectLies on this continuum,You should undertake it with rigour

Pay careful attention tothe research process!

During the following lectures, we will deal with the various phases of this research process

Exam questions: example Fundamental research is better than applied research = B False, it has another purpose

Which of the following statements is wrong? = A Research can be a mix of the 2, not extreme fundamental or extreme applied. Because fundamental research can be applied in practiceChapter II - Research Topic The starting point of research process = research topic In general, a good research topic should meet the criteria of Capability is it feasible? Appropriateness is it worthwhile?What is a good research topic? Youre able to do the researchExamples:For the master thesis @ HUB, Sarah would like to investigate whether information about the financial crisis influences the spending behavior of Mexican politicians during 2012-2013. Language issue; need to learn how the talk Spanish in a short amount of time The time; dont have the time to talk about their private life The currency; still value in this current period The financial resources; money to go to Mexico Access to the data; meet the politicians Not always link to theory, whit a new subject.Capability: is it feasible? Fascination Research skills Time Currency Financial resources Access to dataAppropriateness: is it worthwhile? Fit with the specifications and standards of examining institution Clear link to theory Clear research questions Fresh insights Fit with idea you have been given Fit with career goalsThe starting point of research process = Research topicMore concrete?ProblemdefinitionResearchQuestion(s)

First of all define your problem Problem can be situated in the practical context Indicate the value of the problem

Define your research question in order to give more direction to your research process

Problem definition

Research question(s)

ResearchObjective(s)

What? Operationalize how you intend to conduct your research by proving a set of coherent and connected steps to answer your research questionWhy? - Likely to lead to greater specificity compared to research question Require more rigorous thinking What kind of work do I need to do in order to answer my research question? What successive steps do I need to take in order to answer my research question? These are statements, not questions These are numbered in a listExamples:As sales manager, you notice that your sales staff becomes less and less motivated to sell the companys products. Therefore, you decide to investigate in which way you could increase the level of motivation among your sales staff.

To define the concept of motivation To review key literature on the existing measures to motivate sales people To identify the strengths and weaknesses of the identified measures To determine which measures are most relevant to use in the context of my company To carry out primary research in my company to measure the effectiveness of the selected measure

Research question vs. hypothesesResearch questionHypotheses

Core question of your projectWhat is your expectation from your research?

Refer to a specific problem with which the researcher is confronted and to which an answer should be foundAn unproven statement or proposition about a factor or phenomenon that is of interest to a researcher

EXAMPLES

Is there a relationship between European regions and the consumption of candy?People living in South-Europe consume more candy compared to people living in North-Europe

HypothesesRefer explicitly to the existence of a/no relationship between variables there is no relationship between variables E.g., country of origin (Belgium vs France) and the extent of candy consumption are not related.

there is a relationship between variables (no specified direction) E.g., country of origin (Belgium vs France) and the extent of candy consumption are related.

there is a relationship between variables in a specified direction E.g., Belgium shows higher levels of candy consumption compared to France.COMMON MISTAKES to avoid!!!!! Ambiguous formulation E.g., Belgians consume much candy, Americans dont. Be more specific No point of reference E.g., adolescent consumer much more alcohol Need reference support Unfounded Theory as fuel Based on your feeling, try to take advice from existing theoryExample Belgian adolescent have a better self-image vs. French adolescent this is a little bit ambiguous self-image we dont know what they refer to (its a little bit ambiguous) CaseThe Flemish government wants to reduce the annual amount of waste with 1.5 percent. This is only possible if less waste is produced by Flemish households. Therefore, the government asked CEDON, a research center of HUB to examine how to tackle this issue. To do General research question How the waste is produced? How the Flemish households are structured? 2 specific research question How many of the waste can be recycled? How many of the waste is produced in Flemish Households 2 relevant research objectives To analyses the amount of waste in Flemish households To define different types of waste to reduce the amount 2 hypothesis/ relevant! Households in suburban areas produce more waste than households in rural areas Households with children produce more waste than the one without childrenWhat is the role of theory and literature review in this phase of the research process?Theory is about how or more variable are related to each others. Why do literatures review? To see if there is a gap in literature so that your research question till that gap, to check if there was already some research about your topic and to come up with insightful outcomes.Chapter III a Research Design

How will we investigate our research topic? How will you define your research?

DEVELOP THE RESEARCH DESIGN!

What is research design?Its a kind of plan that you make, you look at the step to get information to respond to your research. Research design is a general framework or plan for conducting a research project; it details the procedures necessary for obtaining the information needed to answer the research question(EXAM: if you are able to motivate your answer and explain in a good way for her is OK)MOTIVATE the choices you make!!!!!What is a research paradigm?It is a broad philosophical theory terms, it is about a philosophical way to investigate, measure the knowledge that you have for your research project.NL: the development of your research topic design will be influenced by your research paradigmThe difference between research paradigms is based on assumptions within three domains: Ontology: what is the reality? how does reality look like? Is there a reality external to humans? If yes, what does it look like? Epistemology: how can we built knowledge about that reality? How do we know what we know? What counts knowledge, what doesnt? How is the relationship between research and subject? Methodology: how can the researcher acquire knowledge about his beliefs? Is limited by ontological and epistemological viewpoints.

POSITIVISm CONSTRUCTIVISMListening from a distanceempathic listeningWhat is the truth?What do stories tell about the story teller and his/her context?What are the facts behind the stories?

YOUTUBE VIDEO research paradigmA paradigm is a lens or way of thinking about the world, when you think about a paradigm think about looking through colored glassed. This lens directs everything we do as a researcher. In research there are two paradigms. Lets think about the qualitative paradigm like looking through pink glasses and the quantitative paradigm like looking through blue glasses, each paradigms as a specific ontology (= beliefs about reality. Ontology refers to perspectives of reality. Is there a truth to be discovered? Realism is the ontology within the quantitative paradigm. Realists believe that there is a truth about reality waiting to be discovered. Within the qualitative paradigm the ontology is referred to as relativism; relativists believe that there are multiple perspectives of reality.Within each paradigm there are also distinct epistemologies or approaches to research, the epistemology determine the relationship between the researcher and the researched (epistemology = approaches to knowing ) The quantitative or positivist approach to knowing is based on realist ontology. Researchers approach knowledge discovery from an etic perspective, this means that researchers are on the outside striving for an objective measure of the topic The qualitative or naturalist approach to knowing is based on relativist ontology; qualitative researchers do not believe that reality is fixed, but that there are multiple perspectives of reality. They assume that knowledge is maximized by increasing the proximity between the researcher and the researched. An emic or insiders approach to knowledge discovery is used. Reality is co-constructed.A methodology is an approach to obtaining information which is informed by theories (= methodology ways of obtaining evidence/knowledge) and developed into a rigorous form of inquiry. The methods undertaken are only one component of a methodology. In general quantitative studies will have either experimental or non-experimental studies and use a scientific method. This paradigm often considers the phenomena in an objective way. The goal is to discover the truth. Qualitative research generally occurs in a natural environment where reality is constructed, therefore it is also called a constructive paradigm. The focus of qualitative research is subjective in nature. The goal is to uncover perceptions of reality, despite ontological, epistemological and methodological differences; researchers share common overall goals and face many similar challenges. Both paradigms have similar goals of gaining understanding and developing evidence. They each have ethical constraints and follow ethical principles all studies, regardless of their paradigm, have limitations. Every research question can be answered in different ways. No study can ever definitely answer a research question. Each study adds to the body of accumulating nursing evidence.

POSITIVSMCONSTRUCTIVISM

Explaining (causal) relationshipUnderstanding subjects meaning

Objective processIntersubjective process

Knowledgeable researcher, known subjectsResearchers becomes involved with subjects

Verification of theoryTheory building

Mainly deductiveMainly inductive

Often (not always) quantitativeOften (not always) qualitative

Which of the following statements is false? Ca) Is correct IF you said that it can start with a deductive approach and finish with a inductive.b) Qualitative is more induction BUT can be deduction as well.c) FALSE research paradigm try to intervenes in a positive wayd) If you would like to know peoples motivation you may be given a questionnaire instead or an interview (to have a honest answer do a qualitative research)

Are the following statements true or false?a) The positivist paradigm focuses on the subjective meaning of the subject under investigation false you have to look at the reality in a objective way he/she would not intervenes in a subjective view, look at the fact that he/she can generalize the populationb) Which research paradigm is better depends on the research question(s) you are seeking to answer. true research question is about motivation, in that case is better to focus on a positivism research paradigm if you really are looking for a number to generalize the population youll probably be better with a positivist paradigmResearch paradigm influences the design that you would use. If you are a positivist person you are more likely to use quantitative method, and you will probably use a quantitative data collection method into your design, but your research approach also influence the element of your research design. Research approach is about deduction vs. inductionResearch approachThe development of your research design will also be influenced by your research approach

DEDUCTIONINDUCTIONTheoryTheoryDataData

Building theoryTesting theory

Deduction and induction can be combined within the same research project!

The deduction, you start with hypothesis and finish with the data. Into your research design it is more likely that a questionnaire or experiment would be used. Induction you start with data base on that you focus on the theory in that case it is more likely that you would use a qualitative data in that case induction will influence your research approach. Again it can be a combination but it is more likely like describe above.

Positivism deductionConstructivism induction

Explaining (causal) relationshipsUnderstanding subjects meaning

Objective processIntersubjective process

Knowledgeable researcher, known subjectsResearcher becomes involved with the subjects

Verification of theoryTheory building

Mainly deductive Mainly inductive

Often (not always) quantitativeOften (not always) qualitative

Some practical criteria of deduction or induction: Emphasis of the research and nature of the research topic / Wealth of literature induction, you dont know the domain you have to explore it. Better use the induction research approach Time available induction would take more time, because you have to gather every data to develop the theory Risk induction, you dont know what is the theoretical inside. Because you are collecting all information to see if it fit with the theory Audience in many case if you work into a company and make a research you are more likely to use a deduction approachExam questions: example Is the reasoning below in line with an induction or deductive approach? Deduction because the car is outside so it will become wet If it rains, everything outside becomes wet, it rain, the car is outside the car will become wet Is the reasoning below in line with an induction or deductive approach? Induction because the data says all the duck would be brown First duck in the park is brown, the second duck in the park is brown, the third duck in the park is brown every duck in the park is brownWhich of the following statements is true?a) With deduction, data are collected and a theory developed as a result of the data analysis.b) Research projects should include either the deductive or inductive research approachc) A research topic about which little literature exists, is more likely to result in an inductive research approach than a deductive research approachd) The deductive research approach is less strict compared the inductive research approach.

Patrick is a member of the Human Relation Research Group of HUB. He read about the large amount of adolescents slipping into shoplifting behavior and wonders how this behavior cold be prevented. Therefore, he runs a study in which he tests whether the Protection Motivation Theory is applicable to this particular issue. Patricks study leans towards: An inductive research approach A deductive research approach he start with the theory and gather some date and then hes going to say if the theory is applicable yes or noResearch paradigms & research approach

Research design

Ok but which aspects are to be considered when developing my research design?Research design: elements to be considered Think in systematic way to collect data Recognizing the nature of your research design (i.e., research purpose) EXPLANATORY DESCRIPTIVEEXPLANATORY Choosing a quantitative, qualitative or multiple methods research design (i.e., research choice) Choosing a research strategy or strategies Establishing the ethics of the research design Choosing a time horizon Establishing the quality of the research designExplanatory research to discover what is happening and gain insights about a topic of interest. It is particularly useful if you wish to clarify your understanding of a problem, such as if you are unsure of the precise nature of the problem. It may be that time is well spent on exploratory research, as it might show that the research is not worth pursuing! () has the advantage that it is flexible and adaptable to change It establish relationship between variables E.g., quantitative study investigating whether certain colors in the shop lay-out result in higher levels of customer satisfaction E.g., qualitative study investigating whether Corporate Social Responsibility in a company influence employee involvementRecognize the nature of your researchResearch projects may serve more than one purpose!

EXPLANATORYDESCRIPTIVEEXPLORATORY

AN EXAMPLE: Cinite, I., Duxbry, L.E. & Higgins, C. (2009). Measurement of perceived organizational readiness for change in the public sector. British journal of Management, 20 (2), 265-277. Exploratory phase: to identify behavior, based on participants experiences, of organizational change (interviews) Descriptive phase: used as a forerunner for the next phase (web-based survey) Explanatory phase: to explain the relationship between organizational actions and readiness or unreadiness to implement change based on employees perceptions. (web-based survey)

Example questions: exampleWhich of the following statement is false?a) Profiling HUB-students in terms of gender and age is an example of descriptive research.b) Exploratory research may follow descriptive or causal researchc) When little is known about the problem situation, it is desirable to start with exploratory researchd) Investigating whether a decrease in price leads to increased sales and market share results in descriptive researchResearch design: elements to be considered Recognizing the nature of your research design (i.e., research purpose)Choosing a quantitative, qualitative or multiple methods research design Choosing a research strategy or strategies Choosing a time horizon Establishing the ethics of the research design Establishing the quality of the research designChoosing a quantitative, qualitative or multiple methods research design How will you combine quantitative and qualitative data collection techniques and data analysis procedures? Quantitative Often used as a synonym for data collection techniques/data analysis procedures that generate or use numerical data Qualitative Often used as a synonym for data collection techniques/data analysis procedures that generate or use non-numerical dataThis distinction is both problematic and narrowQuantitative vs. Qualitative: problematic distinction Why problematic? Many research designs are likely to combine quantitative and qualitative elements E.g, research design using a questionnaire in which respondents also have to answer some open questions in their own words E.g, qualitative research data may be analysed quantitatively (i.e, qualitative data being quantised)Quantitative vs. Qualitative: narrow distinction Why narrow? Reinterpret quantitative and qualitative methodologies through their associations to research paradigms, research approaches and research strategiesQuantitative research designQualitative research design

Research paradigm: positivismResearch paradigm: constructivism

Research approach: deductionResearch approach: induction

Characteristics: causal relationships, numbers, statistical analysis techniques, standardized, probability sampling, generalizability, independent researcher, Characteristics: meanings, text, interpretation, non-standardized, non-probability sampling, develop conceptual framework, researcher part of research process,

Research strategies: experiments, surveys, Research strategies: case study,

Why narrow? Reinterpret quantitative and qualitative methodologies through their associations to research paradigms, research approaches and research strategiesStill, it is perfectly possible that a quantitative research design is more in line with induction, and that a qualtitative research design is more in line with deduction, !!!

Choosing a quantitative, qualitative or multiple methods research design Many research designs are thus likely to combine quantitative and qualitative elements Mono methods use of single data collection technique and corresponding analys procedure(s) Multiple methods use of more than one data collection technique and analysis procedure(s) Multi-method use of more than one data collection technique and corresponding analysis procedure(s), but restricted within either a quantitative or qualitative research design Multi-method quantitative studies use of more one quantitative analysis procedure(s) Mixed-method use of both quantitative & qualitative data collection techniques and analysis procedures Mixed-method research use of quantitative and qualitative data collection techniques and analysis procedures either at the same time or one after the other but not in combination Mixed-model research use of quantitative and qualitative data collection techniques and analysis procedures as well as combining these quantitative and qualitative approaches

Advantage of using more than one data collection technique and analysis procedure? Triangulation: multiple methods may be used in order to combine data to ascertain if the findings from the other method Confidence: findings may be affected by the method used. Use of a single method will make it impossible to ascertain the nature of that effect. To seek to cancel out this method effect, it is advisable to use more than one method. This should lead to greater confidence in your results. Different methods for different research purposesChoosing a quantitative, qualitative or multiple methods research design Whatever methods you use to collect and analyse data Be explicit about the grounds on which multiple methods research is conducted! And do not forget that they must serve your research questionExam question: exampleSuzan chooses to collect numerical data using both a standardized questionnaire and an experiment. She analyses these data using statistical procedures. This is an example of: A multi-method quantitative research A multi-method qualitative research Mixed-method research Mixed-model researchResearch design: elements to be considered Recognizing the nature of your research design (i.e., research purpose) Choosing a quantitative, qualitative or multiple methods research design (i.e., research choice)

Choosing a research strategy or strategies

Choosing a time horizon Establishing the ethics of the research design Establishing the quality of the research designChoosing a research strategy or strategies Various research strategies exist:Choice of research strategy (strategies) is guided by research question, research objectives, research paradigm, research approach and research purpose, as well as by more practical concerns (e.g., existing knowledge, time and other resources, access to potential participants and other sources of data).

Experiment Survey Archival research Case study Ethnography Action research Grounded theory Experiment Commonly used to infer causal relationships Simply stated, to interfere whether a change in one or more independent variables produces a change in one or more dependent variables.Classic experiment

Participants randomly assigned to either the experimental group of control group Each group should be similar in all aspects relevant to the research other than whether or not they are exposed to the planned intervention or manipulation.Experimental group: some form of planned intervention/manipulation will be testedControl group: No such intervention/manipulation is made try to control the possible effects of alternative explanations to the planned intervention/manipulation eliminate threats to internal validity

Classic experiment Between-subjects design =Participants belong to either to experimental or control group but not to both

Post-test measurementPre-test of purchasing behaviorMeasurement ofPurchasing behavior Buy two get one free Promotion: yes or no

ExperimentInternal validityThe extent to which the findings can be attributed to the interventions rather than any flaws in your research design

External validity Whether the cause-and-effect relationship(s) found in the experiment can be generalizedChapter 3B: Research designExam questions: example As marketer, you are wondering whether rock versus pop music in supermarkets influences the time consumers spend in these supermarkets. Design an experiment which would enable this marketer to find an answer on his problemChoosing a research strategy or strategies ExperimentChoice of research strategy (or strategies) is guided y research questions, research objectives, research paradigm, research approach and research purpose, as well s b more practical concerns (e.g, existing knowledge, time and other resources, access to potential participants and other sources of data)

Survey Archival research Case study Ethnography Action researchThese strategies are not mutually exclusive (e.g., survey might be used as part of case study)

Survey Involves the structured collection of data from a sizeable population

QuestionnaireStructured observationStructured interviews

Usually associated with the deductive research approach Popular and common research strategy in business and management research Most frequently used to answer what, who, where, how much, and how many questionsSurvey Collection of standardized data from a sizeable population in highly economical way, allowing easy comparison Perceived as authoritative by people in general Easy to explain and to understand When sampling is used, it is possible to generate findings that are representative of the whole population at a lower cost than collecting the data for the whole population

Survey Data collected by the survey strategy is unlikely to be as wide-ranging as those collected by other research strategies Limited number of questions can be included In case a questionnaire is used Capacity to do it badly

Survey: exampleStudent Employment Survey(Stef Adriaenssens, Dieter Verhaest, Anja Van den Broeck, Karin Proost, Dries Berings)

In the past decades, social-scientific research about student employment has quite effectively documented the positive as well as adverse effects of student employment on well-being and school performance. In short, adolescent students employment concurrently poses considerable threats to and holds significant opportunities for educational outcomes. The outcome is mainly determined by the intensity and the quality of the job. Two elements are lacking, however, in order to apply these insights to school and labor market policies: precise knowledge of local labor markets of adolescent student employment on the one hand and empirically well informed policy guides for schools on the other. Provided that these elements are available, the basis to minimize adverse effects and maximize positive outcomes through everyday school policy would be within hand reach. In order to fill this gap, the Student Employment Survey (SES) has been set up. This survey has been conducted during the last months of 2010 among a representative group of secondary education students in Flanders.

Choosing a research strategy or strategiesVarious research strategies exist:Choice of research strategy (or strategies) is guided y research questions, research objectives, research paradigm, research approach and research purpose, as well s b more practical concerns (e.g, existing knowledge, time and other resources, access to potential participants and other sources of data)

Experiment Survey Archival research Case study Ethnography Action research

Archival research Analysis of administrative records and documents as principal source of data because they are products of day-to-day activities Recent as well as historical documents secondary data analysis Data are part of the reality being studied rather than having been collected originally as data for other (research) purposes Allows research questions which focus upon the past and changes over time to be answered Disadvantages might be the nature of the records and document, missing data, and access to data (confidentiality )Choosing a research strategy or strategies:Various research strategies exist:Choice of research strategy (or strategies) is guided y research questions, research objectives, research paradigm, research approach and research purpose, as well s b more practical concerns (e.g, existing knowledge, time and other resources, access to potential participants and other sources of data)

Experiment Survey Archival research Case study Ethnography Action research

Case study Empirical investigation of a particular contemporary phenomenon within its real-life context, using multiple sources of (data) evidence The boundaries between the phenomenon being studied and the context within it is being studied are not clearly evident

Experiment: research undertaken in a highly controlled context

Surveys: ability to explore and understand the context is limited by the number of variables for which data can be collected Relevant strategy if you wish to gain a rich understanding of the context Has considerable ability to generate answers to why, what and how questions Likely to use multiple sources of data ( interviews, observation, documentary analysis, questionnaires,)

TRIANGULATIONThe use of two or more independent sources of data or data colection methods within one study in order to help ensure that the data are telling you what you think they are telling you

Actually, the term triangulation is often used various waysData (sources) triangulation The use of multiple data sources in the same study Three possible types are tie, space and person The robustness of data can vary based on the time data were collected, people involved in the data collection process and the setting from which the data were collected slices of data at different times and (social) situations, as well as on a variety of people, are gatheredData (sources) triangulationFor example, suppose you are evaluating an afterschool program that you are overseeing. You would first identify the stakeholder groups such as youth in the program, their parents, school teachers, school administrators, afterschool program staff and volunteers. You decide to conduct in-depth interviews to gain insight on what the stakeholders perceive as outcomes of the program. You would then interview representatives of each stakeholder group. You would triangulate by looking for outcomes that are agreed upon by all stakeholder groups. The weight of evidence suggests that if every stakeholder, who is looking at the issue from different points of view, sees an outcome then it is more than likely to be a true outcome.

Theoretical triangulation The use of multiple theories in the same study Both related and/or competing theories can be used e.g., Criminology, social psychology, behavioral economics, to explain the results of a shoplifting studyInvestigator triangulation The use of more than one researcher in any of the research stage s of the same study E.g., the use of multiple observers, interviewers, data analysts, in the same study(Data) analysis triangulation The use of more than one method to analyze the same set of data E.g., Analyzing results from semi-structured interviews by means of SPSS and by means of qualitative data analysis technique (e.g., open, axial, selective coding)Methodological triangulation The use of more than one method for gathering data in the same study Often used to indicate the use of both qualitative and quantitative data collection methods E.g., Using interviews, focus groups and questionnaires to study the relationship between CSR-activities and employee involvement Exam questions: example Suppose a researcher is involved in exploratory qualitative research. More specifically, he uses in-depth interviews as well as focus group to explore factors that motivate shoplifting among adolescents. Which types of triangulation is used by this researcher: data sources, investigator, theoretical, data analysis or methodological triangulation?Case study Four case study strategies Single case vs. Multiple case Single: critical, extreme, unique, typical, hardly investigated, case Multiple: can findings be replicated across cases? Holistic case vs. embedded case (unit of analysis?) Holistic: e.g., organization as a whole Embedded: e.g., sub-units within the organizationCase study: example Building high quality interaction and cooperation during organizational change (Grieten & Lambrechts, 2007, 2009) Problem definition: 2/3 of change processes fails, although it is known that these failures are often caused by relational aspects Research question: What makes relational practices of such a quality that they improve common progress during organizational change? Case selection: Two organizations with contrasting change processes in terms of results (best practice and worst practice), but similar in terms of relational approach Data collection methods: (participant) observation, in-depth interviews, focus groups, document analysis (Triangulation!)Choosing a research strategy or strategies Various research strategies exist:Choice of research strategy (or strategies) is guided y research questions, research objectives, research paradigm, research approach and research purpose, as well s b more practical concerns (e.g, existing knowledge, time and other resources, access to potential participants and other sources of data)

Experiment Survey Archival research Case study Ethnography Action research

Ethnography Used for studying people in groups, who interact with one another and share the same space (e.g., street level, work group, organization, ) Origins in (colonial) anthropology Focuses upon describing and interpreting the social world through first-hand field study Research living amongst those whom they study, to observe and talk to them in order to produce detailed cultural accounts of their shared beliefs, behaviors, interactions, language, rituals and the events that shaped their lives Ideas about this strategy or not unified!Choosing a research strategy or strategies Various research strategies exist:Choice of research strategy (or strategies) is guided y research questions, research objectives, research paradigm, research approach and research purpose, as well s b more practical concerns (e.g, existing knowledge, time and other resources, access to potential participants and other sources of data)

Experiment Survey Archival research Case study Ethnography Action research Grounded theory

Action research An emergent and iterative process of inquiry tat is designed to develop solutions to real organizational problems through as participative and collaborative approach, which uses different forms of knowledge, and which will have implications for participants and the organization beyond the research project research in action rather than research about action Demanding strategy in terms of the intensity involved and the resources and time required

The action research spiral

Choosing a research strategy or strategies Various research strategies exist:Choice of research strategy (or strategies) is guided y research questions, research objectives, research paradigm, research approach and research purpose, as well s b more practical concerns (e.g, existing knowledge, time and other resources, access to potential participants and other sources of data)

Experiment Survey Archival research Case study Ethnography Action research Grounded theory

Grounded theory Develop as a response to the extreme positivism of past social research Theory is developed through the systematic and simultaneous process of data collection and analysis involving a mainly inductive approach To generate theory grounded in your data A process of constant comparison moving between inductive and deductive thinking Abduction = research approach involving the collection of data to explore a phenomenon, identify themes and explain patterns, to generate a new (or modify an existing) theory which is subsequently tested Theoretical sampling until theoretical saturation is reacheda) = conceptual densityb) = conceptual saturation Time-consuming, intensive and reflective Will something significant emerge?a) Will something emerge that is more than simply descriptive?Grounded theory: example Nyilasy, G., & Reid, L.N. (2009). Agency practitioners metatheories of advertising. International Journal of Advertising, 28(4), 639-668 What do advertising agency practitioners think about how advertising works? This studys basic aim was to understand practioners thinking about the work of advertising in their own terms. As there was little substantive research of this perspective, a grounded theory approach to qualitative research was used Semi-structured, in-depth interviews were used until theoretical saturation was achievedExam questions: example Is the following statement true or false? Explain your answerA case study strategy enables the researcher more to gain insight into the context of the research phenomenon compared to a survey strategy

Which of the following statements is wrong?a) Abduction is an approach in which deduction and induction are combinedb) The main goal of the grounded theory research strategy is to generalize findings from a sample to the whole populationc) Experiments consider less contextual elements of the research phenomenon compared to case studiesd) The grounded theory research strategy leans more towards the constructivism paradigm than the positivism paradigm Suppose a researcher is involved in exploratory qualitative research. More specifically, he uses in-depth interviews as well as focus groups to explore factors that motivate shoplifting among adolescents. The researcher is not sure about how many adolescents he should interview. Which concept might be useful for this researcher to determine the appropriate sample size?Research design: element s to be considered Recognizing the nature of your research design (i.e., research purpose) Choosing a quantitative, qualitative or multiple methods research design (i.e., research choice) Choosing a research strategy or strategies Choosing a time horizon Establishing the ethics of the research design Establishing the quality of the research designChoosing a time horizon Cross-sectional studies The study of a particular phenomenon or phenomena at a particular time, i.e., a snapshot Choice of moment may be important Longitudinal studies The study of a particular phenomenon or phenomena over an extended period of time (different moments in time) Possible to study changes and developments Be careful for relevant changes in variables you dont take into account! E.g., consumer sentiment Index (University of Michigan) Which of the following statements is false?a) Longitudinal research has the capacity to study change and development b) Archival research is not only for historians, but also for those involved in management and business researchc) Action research is concerned with the resolution of organizational issuesd) Qualitative research is cross-sectional and not longitudinal in natureResearch design: elements to be considered Recognizing the nature of your research design (i.e., research purpose) Choosing a quantitative, qualitative or multiple methods research design (i.e., research choice) Choosing a research strategy or strategies Choosing a time horizon Establishing the ethics of the research design Establishing the quality of the research designEstablishing the ethics of the research design Choice of research topic and design is governed by ethical considerations The research design should not subject the research population to the risk of embarrassment, harm, or any other material disadvantage E.g., should the research population be aware of the fact that they are the subject of research?Research design: elements to be considered Recognizing the nature of your research design (i.e., research purpose) Choosing a quantitative, qualitative or multiple methods research design (i.e., research choice) Choosing a research strategy or strategies Choosing a time horizon Establishing the ethics of the research design Establishing the quality of the research designEstablishing the quality of the research design

RELIABILITYVALIDITY

The extent to which research findings are really about what they profess/appear to be aboutRefers to whether your research would produce consistent findings if it was repeated on another occasion or if they were replicated by a different researcher

Examples of threats to reliability Completing a questionnaire just before lunch break may affect the way participants respond compared to choosing a less sensitive time Conducting an interview in an open space may lead participants to provide falsely positive answers compared to a space in which they can retain their anonymity Researcher might be tired and misunderstand some of the more subtle meanings of his or her interviewees RELIABILITY IS ABOUT RANDOM ERROR

Examples of threats to validity Informing participants about a research project may alter their work behavior or response during the research if they believe it might lead to future consequences for them During a study, is was difficult to say if customer satisfaction to a store was caused by frequency of shopping in that shore or if frequency of shopping in a store was caused by customer satisfaction to a store VALIDITY IS ABOUT SYSTEMATIC BIAS

Validity There are many types of validity: for now, we will discuss one type External validity a) Can a studys research findings be generalized to other relevant settings, groups, times,?b) In other words, generalizability of findings to other contextsReliability & validity: an example by means of a scale as measurement instrument1. 73 k2. 73 kg3. 73 kg4. 73 kgRELIABILITY

Real weight = 78 kilo VALIDITY Reliability does not involve validity!!! And validity does not involve reliability!!!

Reliability & validity Reliabilityreliability Validity validity

ReliabilityReliabilityValidityValidity

Exam questions: example The student administration department of HUB examines the extent to which HUB-student are satisfied with the teaching skills of the HUB-staff. By means of a questionnaire on Time 1, researcher X finds that the overall satisfaction is equal to 8.7 on 10. Two weeks later (time 2), researcher X conducts the same research (among the same respondents) and finds that the overall satisfaction is equal to 8.7 on 10. Consequently, researcher Xs results are:a) Validb) Reliable

You developed a measurement instrument to examine employees level of job autonomy perceptions (i.e., the extent to which they experience autonomy in their job). This measurement instrument seems to be sensitive to social desirability (i.e., respondents tendency to give answers that may be desirable from a social standpoint)Questions: what is the implication of social desirability for the quality of your measurement instrument?

Which of the following statement is correct?a) Experiments are more valid compared to surveysb) If a study is reliable, it means that it measures what we think it should measurec) External validity is about the extent to which the reliability of a study can be generalizedd) An interviewed who writes down a wrong answer from absent-mindedness threats the reliability of his studyTo conclude Make sure that you end up with a coherent research design that is in line with your research question Motivate the choices you make when designing your research Research black-and-white (flexibility)Theoretical questions Define the term research design The development of your research design is influenced by your research paradigm. Explain. What is a research paradigm? What are the main differences between the positivistic and constructivistic research paradigms? The development of your research design is influenced by your research approach. Explain Explain the main differences between the deductive and inductive research approach Which practical criteria determine whether the deductive or inductive research approach is most suited? Which elements are to be considered when designing your research? What types of purpose might research projects have? What is descripto-explanatory research? What is the goal of the research choice component of the research design? Quantitative research is about numerical data, while qualitative research is about non-numerical data. Why is the distinction problematic and narrow? What is the advantage of using more than one data collection method and analysis procedure? Which criteria influence the suitability of the various available research strategies? What are experiments about? How a classic experiment does looks like? What is a between-subjects experimental design? What are the following research strategies about: survey, archival research, case study, ethnography, action research and grounded theory? What are the (dis) advantages of a survey strategy? How is a case study different than an experiment and a survey strategy? What is meant by triangulation? Define the following terms: data triangulation, theoretical triangulation, methodological triangulation, investigator triangulation, analysis triangulation. Which four case study strategies do exist? Define the term action research spiral What is mean by abduction? What is theoretical sampling? What is meant with theoretical saturation? What is the difference between cross-sectional and longitudinal studies? What is meant by an ethical research design? Define the terms reliability, validity and external validityChapter 4a: samplingIntroductionResearch question = how many beers do Belgian adults drink a week?Case are not necessarily people!e.g., what is the average price of chicken soup in Chinese restaurants located in Brussels?

Research question = how many beers do Belgian adults drink a week?

You could collect and analyze data from every possible case in the population (= census)

Restrictions in terms of time, money, access, currency, speed, practice, accuracy, detail,

Considering data from a subgroup (= sample) rather than all possible cases or elements of the population

Sampling is about selecting a number of element from a population you would like to study, with the intention to derive characteristics of the population from characteristics of the sampleThe sampling process

Define the population Depends on your research question!a) E.g., how satisfied are HUB-students with the teaching skills of the HUB-professors? Defining the population is not always that straightforwarda) E.g., research project assessing consumer response to a new brand of mens moisturizer

Be careful for population specification error = consequences of not studying a specific part of the target group

Exam questions: example Define the population for the following research questions:a) How do employees of Carrefour think the proposed introduction of compulsory Sunday working will affect their working lives?b) What is the normal range in miles that can be travelled by electric cars in everyday use?The sampling process

Determine the sampling frame = a list of all elements in the population from which your sample will be drawn Examples: Telephone book Companies customer database Membership lists in some cases, you will have to develop the sampling frame yourself!Sampling frame error = sampling frame is not a perfect reproduction of the research population= the variation between the population defined by the researcher and the population as implied by the sampling frame used Examples of causes of sampling frame errors: Not up to date Elements of sampling frame that are not part of the population Elements of population are not in sampling frame Elements that are included multiple timesDetermine the sampling frame Checklist: Are elements listed in the sampling frame relevant to your research question? How recently was the sampling frame compiled, in particular is it up to date? Does the sampling frame includes all elements, in other words is it complete? Does the sampling frame contain the correct information, in other words is it accurate? Does the sampling frame exclude irrelevant cases, in other words is it precise? For purchased lists and online panels, can you establish and control precisely how the sample will be selected? For an online panel, can you establish whether incentives will be used to enhance the likely response and provide an assessment of the impact of this on respondent characteristics and consequently responses?Determine the sampling frame You should not generalize beyond your sampling frame E.g., sampling frame consist of all employees of an organization you can only generalize to employee of that particular organization Sometimes not possible (or very hard) to develop a sampling frame!Exam questions: exampleWhich sampling frame is suited for the following research questions? How do employees of Carrefour think the proposed introduction of compulsory Sunday working affect their working lives? Which factors influence Belgian lawyers decision to work in other European countries?The sampling process

First of all, you need to decide whether you will examine all elements of the population (= census; see introduction) or whether you will draw a sample For populations of fewer than 50, it is usually more sensible to collect data from the entire population. whether you will examine all elements of the population (=census; see intro) or will draw a sample conditions: Practical constraints Budget constraints Time constraints Access constraints Results need to be quickly available Testing includes destroying of population (e.g., establish the actual duration of long-life batteries)Census vs. sample Using sampling might results in higher overall accuracy compared to a census More time can be spent designing and piloting the means of collecting data You can collect information that is more detailed In case you are employing people to collect data (e.g., interviewers), you can afford higher-quality staff You can devote more time trying to obtain data from more difficult to reach cases More time can be devoted to checking and testing the data for accuracy prior to analysisIn case you decide to draw a sample. you can choose between two types of sampling Probability or representative sampling Non-probability samplingProbability sampling techniques Sampling techniques in which each element of the population has a fixed probabilistic chance (usually an equal chance) of being selected for sample

It becomes possible to answer research questions that require you to estimate statistically the characteristics of the population from the sample (i.e., with a certain level of confidence, you are able to generalize the findings to the population)

Consequently, probability sampling is often associated with survey and experiment research strategies.Non-probability sampling techniques The probability of each case being selected from the total population is not known.

It is impossible to answer research questions that require you to make statistical inferences about the characteristics of the populationNote: you may still be able to generalize from non probability samples about the population, but not on statistical grounds.

Sampling techniquesPROBABILITY SAMPLING TECHNIQUESNON-PROBABILITY SAMPLING TECHNIQUES

Simple random samplingQuota sampling

Systematic random samplingJudgmental sampling

Stratified random samplingSnowball sampling

Cluster samplingSelf-selected sampling

Multi-stage samplingConvenience sampling

Exam questions: exampleWhich of the following statements is true?a) With probability samples the chance, or probability, of each case being selected from the population is unknown.b) Generalizations about populations from data collected using any probability samples are based on intuition.c) Sampling provides a valid alternative to a census when it would be not practical for you to survey the entire populationd) The sampling frame gives an overview of all the elements which will be included in your final sampleSample techniquesPROBABILITY SAMPLING TECHNIQUESNON-PROBABILITY SAMPLING TECHNIQUES

Simple random samplingQuota sampling

Systematic random samplingJudgmental sampling

Stratified random samplingSnowball sampling

Cluster samplingSelf-selected sampling

Multi-stage samplingConvenience sampling

Simple random sampling = a probability sampling technique in which each element has a known and equal probability of selection. Every element is selected independently of every other element, and the sample is drawn by a random procedure from a sampling frame

e.g.,- Each element of the sampling frame s assigned a unique identification number (0, 1, 2,)- Random numbers are generated to determine which elements to include in the sample (e.g., by means of a random number table) and until sample size is reachedRandom number table example

Select your first random number at random!

if 78 is read off a second time, it must be disregarded as you need different cases. This means that you are not putting each cases number back into the sampling frame after is has been selected. This is termed sampling without replacement. If a number is selected that is outside the range of those in your sampling frame, you simply ignore it and continue reading off numbers until your sample size is reached.Disadvantages of this procedure: Time-consuming Requires adapted table with sufficient random numbersOther random procedures Computer generated random numbers/ online random number generator (~random number tables) Random telephone numbers Often used when doing computer-aided telephone interviewing (CATI) Dialing telephone numbers at random from an existing database Or random digit dialing + does not consider the telephone book - some households have more than one telephone numberSimple random sampling Sample without (systematic) bias Best used when you have an accurate and easily accessible sampling frame that lists the entire population Disadvantage: these lists are not always available! If your population covers a large geographical area, random selection means that selected cases are likely to be dispersed throughout the area Disadvantage: this sample is not suited if collecting data over a large geographical area using a method that requires face to face contact (high travel costs)Simple random sampling: an exampleJemma was undertaking her work placement at a large supermarket, where 5011 of the supermarkets customers used the supermarkets Internet purchase and delivery scheme. She was asked to interview customers and find out why they used this scheme. As there was insufficient time to interview all of them, she decided to interview a sample using the telephone. Her calculations revealed that to obtain acceptable levels of confidence and accuracy she needed an actual sample size of approximately 360 customers. She decided to select them using simple random sampling.Systematic random sampling = a probability sampling technique in which the sample is chose by selecting a random starting point and then picking every 1th element in succession from the sampling frame

Selecting the sample at regular intervals from the sampling frame

Systematic random sampling: an example

Sometimes not necessary to develop a sampling frame (e.g., every tenth visitor of website) Easy to understand and to explain Despite these advantages, be careful when using existing lists as sampling frames You need to ensure that the lists do not contain periodic patterns! Systematic random sampling is suitable for geographically dispersed cases only if you do not require face-to-face contact when collecting data (= simple random sampling)The impact of periodic patterns on systematic random samplingConsider the use of systematic random sampling to generate a sample of monthly sales from the Harrods store in London. The sampling frame contains monthly sales for the last 60 years. A sampling interval of 12 is chosen.

A high street bank needs you to administer a questionnaire to a sample of individual customers with joint bank accounts Sampling fraction = = you will need to select every second customer on the list The names of the customer list, which you intend to use as the sampling frame, are arranged as depicted below

Stratified random sampling You divide the population into two or more relevant strata based on one or a number of attributes (e.g., gender, income, region, ; these attributes are relevant for you research) In other words, your sampling frame is divided into a number of subsets A random (simple or systematic) sample is then drawn from each of the strata. More concrete Choose the stratification variable(s) These variables need to be relevant for the research problem Stratification needs the results in homogeneity within each strata with regards to the stratification variable(s) Divide the sampling frame into the discrete strata Number each of the cases within each stratum with a unique number Select your sample using either simple random or systematic random samplingStratified random sampling: an exampleSarah worked for a major supplier of office supplies to public and private organizations. As part of her research into her organizations customers, she needed to ensure that both public and private sector organizations were represented correctly. An important stratum was, therefore, the sector of the organization. Her sampling frame was thus divided into two discrete strata: public sector and private sector. Within each stratum, the individual cases were then numbered.Stratified random sampling Dividing the population into a series of relevant strata means that the sample is more likely to be representative, as you can ensure that each of the strata is represented proportionally within your sample. Proportionate stratified random sampling = the sample size drawn from the strata are proportionate to the stratas share of the total population Disproportionate stratified random sampling (oversampling enables separate analyses) Despite the advantages of proportionate and disproportionate sampling, there are some disadvantages as well: Only possible if you can easily distinguish significant strata (in your sampling frame) Extra large of sampling procedure more time more expensive more difficult to explain compared to simple and systematic random samplingCluster sampling all elements of a number of randomly selected clusters are selected more concrete: choose the cluster grouping for your sampling frame Heterogeneity in clusters is important! Cluster = mini universe (e.g., population=football lovers; Cluster=football stadium) Number each of the clusters with a unique number (0, 1, ) Select your sample of clusters using some form of random sampling Select all elements of the selected clustersEvery cluster has an equal chance to be selected random technique Every cluster has an equal chance to be selected random sampling technique Still, the technique normally results in samples that represent the total population less accurately compare to stratified random sampling (make sure that clusters are thus heterogeneous!) Advantage: restricting the sample to a few relatively compact geographical sub-areas (clusters) maximizes the amount of data you can collect using face-to-face methods within the resources availableCluster sampling: an exampleAbdel needed to select a sample of firms to undertake an interview based survey about the use of large multiple-purpose digital printer copiers. As he had limited resources with which to pay for travel andother associated data collection costs, he decided to interview firms in four geographical areas selected from a cluster grouping of local administrative areas. A list of all local administrative areas formed hissampling frame. Each of the local administrative areas (clusters) was given a unique number, the first being 0, the second 1 and so on. The four sample clusters were selected from this sampling frame of local administrative areas using simple random sampling. Abdels sample was all firms within the selected clusters. He decided that the appropriate telephone directories would probably provide a suitable list of all firms in each cluster.Stratified random sampling vs. cluster sampling

Multi-stage sampling Modifying a cluster sample by adding at least one more stage of sampling that also involves some form of random sampling Procedure: Choose the cluster grouping for your sampling frame Heterogeneity in clusters is important! Number each of the clusters with a unique number (0,1,) Randomly select a number of clusters Repeat the above steps (e.g., districts cities neighborhoods streets) Randomly select elements of the most recently selected clustersMulti-stage sampling: an example Laura worked for a market research organization that needed her to interview a sample of 400 households in England and Wales. She decided to use the electoral register as a sampling frame. Laura knew that selecting 400 households using either systematic or simple random sampling was likely to result in these 400 households being dispersed throughout England and Wales, resulting in considerable amounts of time spent travelling between interviewees as well as high travel costs. By using multi-stage sampling, LauraKnew these problems could be overcome. In her first stage, the geographical area (England and Wales) was split into discrete sub-areas (counties). These formed her sampling frame. After numbering all the counties, Laura selected a small number of counties using simple random sampling. Since each case (household) was located in a county, each had an equal chance of being selected for the final sample. As the counties selected were still too large, each was subdivided into smaller geographically discrete areas (electoral wards). These formed the next sampling frame (stage 2). Laura selected another simple random ample. This time she selected a larger number of wards to allow for likely important variations in the nature of households between wards. A sampling frame of the households in each of these wards was then generated using a combination of the electoral register and the UK Royal Mails postcode address file. Laura finally selected the actual cases (households) that she would interview using systematic random sampling.Multi-stage sampling Advantages: Geographically dispersed population becomes possible against lower cost Compared to normal cluster sampling, larger clusters with many cases is possible Disadvantages: Selecting smaller and smaller subgroups might impact the representativeness of your sample Can be solved through applying stratified random sampling techniques as wellImpact of various factors on choice of probability sampling techniques Sampling frame required Size of sample needed Geographical area to which suited Necessity of personal contact with respondent Relative cost Easy to explain to support workers? Advantage compared with simple random sampling Exam questions: example BNP Paribas Fortis has about 400 000 Benelux-clients using their credit card. The credit card application form contains common information such as name, address, age, telephone number, educational level, etc. BNP Paribas Fortis wants to examine whether there is a relationship between the way in which credit cards are used (e.g., frequency of use) and the socio-economic profile of its users.Questions: identify the population and the sampling frame. Consider the suitability of the various probability sampling techniques in this situation

Chapter 4b: samplingSampling techniquesPROBABILITY SAMPLING TECHNIQUESNON-PROBABILITY SAMPLING TECHNIQUES

Simple random samplingQuota sampling

Systematic random samplingJudgmental sampling

Stratified random samplingSnowball sampling

Cluster samplingSelf-selected sampling

Multi-stage samplingConvenience sampling

Remark!In the power point of chapter 4a about sampling, the term self-selecting sampling was used rather than self-selection sampling. Although these terms indicate similar concepts, we prefer to use the term used in the textbook, that is self-selection sampling.

Quota sampling Stratified sampling though the selection of cases is not random (often used for structured interviews as part of a survey strategy) Procedures Divide the population into specific subgroups (quota) based on relevant variables Calculate, based on relevant and available data, for each subgroup the amount of elements to be selected Give each researcher an assignment which states the number of cases in each quota from which they must collect data Combine the data collected by researchers to provide the full sample Quota: Are usually relative to the proportion in which they occur in the population (e.g., 48% female in population 480 females in a sample of 1000 participants)

Precision control= proportions in sample perfectly mirror the proportions in the population

Precision control: example Interest in consumption habits among +16 in a medium village Sample must be representative in terms of residence and age Population: 24 420 16+-residents Sample: 1/12 of population 2035 sample cases 3 districts and 4 age groups 12 quota

Precision control: example Interest in consumption habits among +16 in a medium village Sample must be representative in terms of residence and age Population: 24 420 16+-residents Sample: 1/12 of population 2035 sample cases 3 districts and 4 age groups 12 quota

Precision control: example

Frequency control: representative in terms of criterion

Exam question: exampleAn association has 750 members. In table below, the distribution of these members is given in terms of gender and age

18-2526-4950+

Males 9819178367

Females 75188120383

173 379198750

Draw a quota sample of 125 subjects, taking into account: Gender Males: 125 * (357/750) = 61 Females: 125* (383/750)= 64 Age 18-25: 125 * (173/750) = 29 26-49: 125 * (379/750) = 63 50+ : 125 * (198/750) = 33 Gender & age Males 18-25: 125 * (98/750) = 16 Males 26-49: 125 * (191/750) = 32 Quota sampling Advantages (compared to probability sampling techniques) Less costly Can be set up very quickly Does not require a sampling frame Disadvantage Because the researcher can choose within quota boundaries whom they interview, your quota sample may be subject to bias (e.g., easily accessible respondents who appear to be willing to answer the questions) As the sample is not probability based, you cannot measure the level of certainty, margins of error, Judgmental sampling = purposive sampling You need to use your judgment to select cases that will best enable you to answer your research question Often used when: Working with very small samples (such as in case study research or when you wish to select cases that are particularly informative) E.g., industrial research among experts Doing qualitative researchThose samples cannot be considered to be statistically representative of the total population!

Doing exploratory research

The more common judgmental sampling strategies: Extreme case or deviant sampling Heterogeneous or maximum variation sampling Homogenous sampling Critical case sampling Typical case sampling Theoretical sampling Snowball sampling Commonly used when it is difficult to identify members of the desired population Procedures: Make contact with one or two cases in the population Ask these cases to identify further cases Ask these new cases to identify further new cases (and so on) Stop when either no new cases are given or the sample is large enough Main problem = making initial contact Bias Respondent are most likely to identify other potential respondents who are similar to themselves, resulting in a homogenous sampleSelf-selection sampling It occurs when you allow each case, usually individuals, to identify their desire to take part in the research You therefore: Publicize your need for cases either by advertising or by asking them to take part Collect data from those who respond Problem = representativeness Cases that self-select often do so because of their feelings or opinions about the research questionSelf-selection sampling: examplePatricks research was concerned with the impact of student loans on studying habits. He had decided to administer his questionnaire using the internet. He publicized his research on Facebook in a number of groups pages, using the associated description to invite people to self-select and clicking on the link to the questionnaire. Those who self-select by clicking on the hyperlink were automatically taken to the online questionnaire he had develop using the Qualtrics survey software

Convenience sampling Involves selecting cases haphazardly only because they are easily available (or most convenient) to obtain for you sample E.g., the person interviewed at random in a shopping center for a television program Widely used Though prone to bias and influences that are beyond your control cases appear in the sample only because of the ease of obtaining them bias decreases as the population becomes more homogenous nevertheless, samples ostensibly chosen for convenience often meet purpose sample selection criteria that are all relevant to the research aim. E.g., organization selected to use as a case study that represent, at the same time, a typical case Advantages: Cheap Quick (suited for exploratory research)Impact of various factors on choice of non-probability sampling techniques Likelihood of sampling being representative Types of research in which useful (e.g., non-probability techniques often used in exploratory research) Relative costs (Note: non-probability techniques are often used as the imply less costs compared to probability sampling techniques) Control over sample contents Note: where it is not possible to construct a sampling frame you will need to use non-probability sampling techniques

Exam questions: exampleFor the following research question, it has not been possible for you to obtain a sampling frame. Suggest the most suitable sampling technique to obtain the necessary data, giving reasons for your choice.Research question: would users of the tennis club be prepared to pay a 10 percent increase in subscriptions to help fund two extra tennis courts? You need the answer by tomorrow morning.

For many research projects, you will need to use a combination different sampling techniques!

Exam question: exampleIs the following statement true or false? Give reasons for your answer.stratified sampling can be seen as random quota sampling

The sampling process

Determine the sample size Probability sampling techniques Non-probability sampling techniques

Probability sampling techniquesThe confidence interval approach

First of all, brushing up statisticsNormal distribution

95% of the value/scores is in between -1.96* standard deviation and +1.96* standard deviation

Normal distinction standard normal distributionZ = value mean/standard deviationAZ-score of (-)1.96 corresponds with a confidence level of 95%

Statistical inference Important in research is to calculate statistics, such as the sample mean and sample proportion, and use them to estimate the corresponding true population values (e.g., population mean and population proportion)

Statistical inference: the process of generalizing the sample results to a target population

Confidence intervals We are thus interested in using the sample statistics (e.g., the sample mean) as an estimate of the value in the population An approach to assessing the accuracy of the sample mean as an estimate of the mean in the population is to calculate boundaries within which we believe the true value of the mean will fall

Confidence internvals

Typically, we look at 95% confidence intervals This means that for 95% of the time, the true value of the population will fall within the boundaries of the confidence interval In other words, if you would collect 100 samples, calculated the means and then calculated a confidence interval for that mean, then for 95 of the samples, the confidence intervals we constructed would contain the true value of the mean in the population = sample mean = population mean = standard deviation of population = sample size Confidence level (Z)

Determine the sample size = sample mean = population mean = standard deviation of population = sample size Confidence level (Z)

We already determined the level of precision (D) but what about Z and ? Specifying Z is about specifying the level of confidence A 95% confidence level is desired Z = 1.96 Determine (= the standard deviation of the population) Secondary sources, pilot study or (max value-min value)/6

An exampleSuppose a researched wants to estimate the monthly household savings investment more precisely so that the estimate will be within +/- 5 of the true population value. What should be the size of the sample?

An exampleSuppose a researcher wants to estimate the monthly household savings investment more precisely so that the estimate will be within +/- 5 of the true population value. What should be the size of the sample?

Confidence level = 95% Z-value=1.96

Sample size

Your choice of sample size is thus governed by The confidence you need to have in your data: the level of certainty that the characteristics of the data collected will represent the characteristics of the total population. The margin of error that you can tolerate: the accuracy you require for any estimates made from your sample The variability in the population in terms of the variable(s) of interestAlso other factors influence the determination of the sample size, such as Time resources Financial resources Type of data analysis Access Expected response Exam questions: exampleA big company wants to know how much money (in euro) each of its managers spends on lunches per month. They know that the maximum amount of money spent is 700 euros while the minimum is 400 euro. The company wants that the result is accurate in terms of 5 euro and wants to make a prediction witha confidence level of 95%.

How large should be the sample size?

Level of precision = D = 5 Confidence 95% z = 1.96 = 700-400/6=50Sample size determination: proportions

? Population proportion? Secondary sources, pilot study, or conservative (=0.5)

An exampleSuppose a researched is interested in estimating the proportion of households in a particular region that have bought clothes online? What should be the sample size?

Chapter 4c: samplingDetermine the sample size Probability sampling techniques

Non-probability sampling techniques

Non-probability sampling: determine the sample size Formulas of probability sampling techniques Are based on the assumption that the sample cases are randomly selected Formulas are just guidelines Larger sample sizes do not necessarily lead to higher levels of confidence and precision However, take into account Variability in the target group Goal of sampling Importance of research for management/client Or you could consider samples sizes used in similar studies, for instance,

Sampling: the non-response problem In reality, you are likely to have non-responses Possible causes of non-response Refusal to participate Intelligently to respond Inability to locate respondent Respondent located but unable to make contact Possible consequences non-response Lower confidence and precision levels due to smaller sample size Non-response bias: people who refuse differ from actual respondent As part of your research report, you will need to include the response rate:

Total response rate =

Active response rate =

Total and active response rate: example Suzan has decided to administer a telephone questionnaire to people who had left her company over the past five years. She obtained a list of the 1034 people who had left over this period (the total population) and selected a 50 per cent sample. Unfortunately, she could obtain current telephone numbers for only 311 of the 517 ex-employees who made up her total sample. Of these 311 people who were potentially reachable, she obtained a response from 147. In addition, her list of people who had left her company was inaccurate, and 9 of those she contacted where ineligible to respond, having left the company over five years earlier.Total response rate = 147/ (517-9)= 28.9%Active response rate = 147/311-9=48.7%

Estimating response rates and actual sample size required Non-response = reality you should estimate the likely response rate and increase the sample size accordingly First of all, determine the minimal sample size (taking into account certain confidence and precision levels) Second, estimate the likely response rate Third, calculate the actual sample size you require

Estimating response rates and actual sample size required: examplePeter was a part-time student employed by a large manufacturing company. He had decided to send a questionnaire to the companys customers and calculated that a minimum sample size of 439 was required. From previous questionnaires that his company had used to collect data from customers, Peter knew the likely response rate would be approximately 30 per cent. Using these data he could calculate his actual sample size: Peters actual sample size, therefore, needed to be 1463 customers. The likelihood of 70 per cent non-response meant that Peter needed to include a means of checking that his sample was representative when he designed his questionnaire.Estimating response rates and actual sample size required

Consider the response rates achieved for similar research that has already been undertaken Beware, response rates can vary considerably when collecting primary data! E.g., postal questionnaires: often lower than 50% E.g., face-to-face contact: often higher E.g., online questionnaires: often lower than 30% Alternatively, err on the side of caution In reality, you are likely to have non-responses Possible causes of non-response Refusal to participate ineligibility to respond Inability to locate respondent Respondent located but unable to make contact Possible consequences of non-response Lower confidence and precision levels due to smaller sample sizeIncreasing the actual sample size useful in case non-response only results in less confidence and precision

However, increasing the actual sample size is no solution When doing longitudinal research in which the same respondents need to be re-examined If it is a matter of non-response bias Refusers differ on observable characteristics (gender, education,) compared to respondents Refusers might also differ on non-observable characteristics!How to trace non-response bias? Comparing characteristics of respondents with refusers On moment of refusal Afterwards by means of additional contact Comparing characteristics of respondents with population Still not the solution when there would be differences in terms of non-observable characteristicsHow to tackle non-response bias? Increasing the number of contacts Work with substitutes that are randomly selected, but which match on crucial characteristics (e.g., gender) However, this measure is not able to solve the bias completelyThe sampling process

Validate the sample Once data are collected from a sample, comparisons between the structure of the sample and the structure of the population should be made If it is found that the structure of a sample does not match the target population (due to population specification error, sampling frame error, sample selection bias, non-response bias) A statistical procedure that attempts to account for these errors/biases by assigning differential weights to the data depending on the response rates.Weighting Each case in the database is assigned a weight The effect of weighting is to increase or decrease the number of cases in the sample that possess certain characteristics Most widely used to make the sample data more representative of a target population on specific characteristics Also used to adjust the sample so that greater important is attached to participants with certain characteristics Because it destroys the self-weighting nature of the sample design, this procedure should be applied with caution! Do not forget to report this procedure!Theoretical questions Explain the following terms: Sampling Population Case/element Census Sample Population specification error Sampling frame Sampling frame error Why do researchers make use of samples rather than examining the whole populations? Define the steps of the sampling process Which are the two main types of sampling techniques? Explain the difference Why does sampling often results in higher overall accuracy compared to examining the whole population? Explain the following sampling techniques and give their advantages as well as their disadvantages: Simple random sampling Systematic random sampling Stratified random sampling Cluster sampling Multi-stage sampling What is a random number table? What are the advantages and disadvantage of such a table? Which random procedure can be used when applying the simple random sampling technique? What is the advantage and disadvantage of random digit dialing? Explain the term sampling fraction What is the difference between a proportionate and disproportionate stratified random sampling technique? What is the difference between cluster sampling and stratified random sampling? Which factors impact the choice of a probability sampling technique? Explain the following sampling techniques and give their advantages as well as their disadvantages: Quota sampling Judgmental sampling Snowball sampling Self-selection sampling Convenience sampling What is the difference between quota sampling and stratified random sampling? What is the goal of the precision control and frequency control procedure? In which situations is purposive sampling often used? What are the more common judgmental sampling strategies? Which factors impact the choice of a non-probability sampling technique? How should the sample size be determined when applying a probability sampling technique? Define the terms statistical inference and confidence interval Which factors influence the sample size in case of probability sampling technique? How should the sample size be determined when applying a non-probability sampling technique? What are the causes and consequences of non-response? What is the difference between the total response rate and the active response rate? How should the actual sample size be calculated? How can the response rate be estimated? Non-response can be tackled by increasing the sample size? However, this procedure is not always the solution explain why not How could non-response bias be traced and dealt with? What happens during the final stage of the sampling process that is the stage in which the sample gets validated? Why do researchers apply the weighting procedure?Chapter 5 using secondary dataWhen thinking about executing your research design to be able to answer your question, you probably think in terms of collecting your own data

Primary data = data collected specifically for the research project being undertaken

However, it is also possible to use secondary data that is data that were originally collected for some other purposeResearch questions might be answered using some combination of primary and secondary data as well!!

Types of secondary data and uses in research May be both quantitative and qualitative data May be raw data (received little if any processing) or compiled data (received some form of selection or summarizing) Primarily used in descriptive and explanatory research (also possible in exploratory research!) Within business and management research, secondary data are most frequently used as part of a case study or survey research strategy (also used as part of other research strategies!)

Three main subgroups of secondary data

Documentary secondary data Often used in research projects that also collect primary data (but you can also use them on their own or with other sources of secondary data!) Include text materials and non-text materials Can be analysed both quantitatively and qualitatively Can be used to help to triangulate findings based on other data Documentary sources you have available can depend on access issues as well as success in locating these sourcesSurvey-based secondary data Data collected using a survey strategy (e.g., questionnaires) that have already been analyzed for their original purpose Collected through one of three distinct subtypes of survey strategy: Censuses Continuous and regular surveys Ad hoc surveysSurvey-based secondary data: censuses Usually carried out by