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Research Methods: An Introduction

Jeroen Moes

These weekly meetings are organised in small seminar groups (max. 25 students, but smaller is even better), unless otherwise indicated in the first column. For seminars involving statistical computer packages, we will meet in a computer lab. The locations of the reflection weeks within the schedule are approximate, because this depends on the specific semester this course would be planned in.


Session #

Main topics

Remarks

1
(lecture)

  • Course outline, explain exercises, grading
  • What is (good) research?
  • What is methodology, and what is it good for?
  • Core concepts in methodology
  • The basic methodological steps to have in a research proposal (for assignments session 2-4)
  • Which approaches will we cover?
  • ASSIGNMENT: For the first seminar (session 2), bring at least two broadly defined topics that you would be interested in studying for this course (some examples given in lecture)
  • Explain the main mission of research generally and this course in particular.
  • Research relevance (i.e. research to solve social problem vs. solve theoretical/empirical problem

2
(seminar)

  • Defining research questions; finding the puzzle, based on topics that students came up with
  • In class experiment: in each seminar group, we will measure the social network relations of students amongst each other at the first meeting (this also serves as a personal introduction of the students). At the end of the course, we will measure again and present the results.
  • ASSIGNMENT: Writing a research proposal (part 1/3: problem statement / puzzle, sub-questions, hypotheses / expectations, social / scientific relevance)
  • Summarize core concepts in research methods from lecture again
  • Seminar tutors should make sure that students define topics that can be used (at least after some modification / selection) within the framework of this course (e.g. they cannot gather very large N data)

3
(seminar)

  • What is data?
  • Which types of data for which types of questions?
  • Who are we asking? Populations, cases, and qualitative saturation.
  • ASSIGNMENT: Writing a research proposal (part 2/3: define your data type, and delimit your ‘population’ (i.e. groups, cases, contexts, etc.))
  • Discussion of research proposal drafts (1/3): problem definition
  • Also a broader discussion on case selection (and issues like methodological nationalism), survey population / universe, and determining number of required interviews in various approaches

4
(seminar)

  • Data collection strategies
  • An introductory overview of approaches to gathering data (i.e. what are the current strategies ‘out there’, and which approaches work best for which types of questions/data?); we will discuss some of these in more detail in later seminars
  • ASSIGNMENT: Writing a research proposal (part 3/3: how could you gather the data you need? Finalize your draft proposal, and hand it in by end of next week)
  • Discussion of research proposal drafts (2/3): data types and definition of ‘population’

5
(lecture)

  • Quantitative and qualitative methods
  • What do these terms mean?
  • A basic introduction to epistemology and ontology
  • A more detailed overview of (typical) quantitative versus qualitative approaches to data collection and analysis, a brief history of this ‘division’ (e.g. Methodenstreit), and how the two are epistemologically/ontologically similar or dissimilar. Are they really fundamentally different? Why / why not?
  • Hypotheses; what they are, and when (not) to use them
  • Basic assumptions for (classic) in-depth, interpretative types of analysis
  • Understanding how arguments are built using in-depth interview data
  • Basic assumptions for (classic) statistical, large-N data analysis
  • Understanding correlations and regression coefficients.
  • A general discussion on causality
  • In this lecture, we will have two pieces of assigned literature (journal articles) as (1) an example of how to ‘read’ regression analysis results and correlation figures, and (2) how an analysis of in-depth interview data is presented in article form
  • From week 5 onwards, the draft research proposals continue to serve as concrete examples and materials for discussion during the seminars

6
(seminar)

  • From research puzzle to interview and survey questions
  • Introduction to designing a (semi-)structured interview guide
  • Introduction to designing a survey
  • Special mention of language and cross-cultural/linguistic research
  • Discussion of research proposal drafts (3/3): data collection strategies
  • In this seminar session, students will have a chance to struggle with coming up with effective questions to use in actual interviews based on each other’s research puzzles and hypotheses (if applicable)

 

7
(seminar)

  • In-depth interviewing
  • How to prepare for an interview and what to do directly before, during, and after an interview
  • How to take notes, recording the interview, and how to transcribe
  • Body languages and verbal cues during interviews
  • Using visual aids during interviews (photography, symbols, etc.)
  • Using maps during interviews, and understanding spatial references: considering the high degree of internationalisation at University College Maastricht, as an exercise, students draw their conceptualizations of ‘Europe’ on blank maps of the continent in class. We then talk about their thoughts and compare those drawings in class to illustrate what this means and how we can use this.
  • A brief overview of software available for transcribing and analysing in-depth data (Atlas.ti, MaxQDA).
  • ASSIGNMENT: For session 9, conduct a short (< 1 hour) interview with at least two people and hand in transcripts (if applicable to your research proposal, you can use this to gather some first data. Otherwise, the tutor will assign a topic for interview)
  • During this session, students will train in-depth interviewing by practicing on each other in mock interviews, and reflecting on their performance
  • Students will receive practical tips and tricks to use during in-depth interviews
  • We will discuss an example of a semi-structured interview guide
  • We will discuss software that exists for this type of research, but we will not go into too much detail on this point

 

(Reflection week)

 

8
(lab session seminar)

  • Correlations and regression analysis
  • What are correlations, what is regression analysis (recap from lecture)?
  • Sources of data: gathering data vs. using existing data
  • An overview of commonly used statistical packages (SPSS, Stata, SAS, R)
  • Starting SPSS
  • Loading data into SPSS (example data is provided)
  • Understanding the ‘variable view’ and the ‘data view’
  • Producing descriptive statistics, and interpreting the output
  • Calculating correlations, and interpreting the results
  • Running a univariate and multivariate linear regression, and understanding the output
  • ASSIGNMENT: For session 9, think of a causal relationship you would like to test using the example data used today. Then, based on the instructions given during this session, produce the descriptive statistics, correlation, and linear regression coefficients for this relation. Offer a brief interpretation of the output (if applicable to your research proposal, you can use this to explore your research questions further. In this case, the tutor will help you with this in more detail).
  • During this session, we will have a very basic introduction to SPSS (or similar package on UM computers) using an existing survey dataset (like EB or EVS)
  • This session will take the form of a step-wise instruction and basic in-class assignments
  • Students will be made aware of the possibility of using syntax, but within the class we will focus on using the menus.
  • The take-home assignment here is likely to cause some frustration, but will also encourage students to ‘tinker’ with the data, variables, and commands. In the next session, we will discuss the issues they ran into.

9
(seminar)

  • Discussion of problems and experiences with conducting in-depth interviews
  • Discussion of problems and experiences with conducting statistical procedures
  • Time for reflection on draft research proposals, and additional individual help from tutor
  • ASSIGNMENT: We will now start rewriting our draft research proposals into (small) projects that can be executed in a few weeks with the resources at our disposal. This can be done individually, but also in groups of up to 3 students (which may increase possibilities for data collection, for example, but may also complicate the process in other ways). Form groups (if applicable) and rewrite draft research proposals by session 11.
  • Hand in (at least) two interview transcripts
  • Hand in descriptive statistics, correlation, and regression analysis

10
(lecture)

  • An overview of specific / specialized methods and techniques in present-day social science
  • Which approaches are currently being used in the social sciences with regard to data collection, data analysis, and data presentation / visualisation?
  • In particular, methods and techniques we will try to cover in the broad overview:
    • Alternative types of regression analysis, and what they are used for (multilevel, logistic)
    • Factor analysis
    • Time analysis & statistics
    • Ethnography and (participant-)observation
    • Focus groups
    • Experimental designs (‘natural’ & ‘controlled’)
    • Framing analysis
    • Archival research
    • Process-tracing
    • Visual methods, and using photography
    • Social Network Analysis
    • Online research methods / ‘Big Data’
  • Data presentation aspects we will cover specifically:
    • Representing spatial data (maps) for both quantitative and qualitative studies
    • Representing quotes from interviews
    • Graphs and tables (do’s and don’ts)
    • Animated graphs and maps
  • The methods and techniques covered in this lecture are offered to students as an introduction. They should know that these approaches exist, what they are typically used for, and in some cases how to interpret their results. They do not need to be able to apply these methods and techniques themselves after taking this course (though experimentation with them in their individual assignments is certainly encouraged).
  • Students are expected to take into account the data presentation aspects from this lecture into their final research reports.
  • Students will receive a hand-out that covers these basics as a future reference sheet and starting point for their individual projects. This includes suggested software and literature to use for independent study.

11
(seminar)

  • Individual meetings with tutor to discuss rewritten draft proposals; continue work on finalizing proposal before execution
  • Hand in rewritten draft research proposals (group or individual)

12
(seminar)

  • Individual meetings with tutor to discuss final research proposals; start data collection and analyses after tutor approval
  • Hand in final research proposals (group or individual)
  • During the next three sessions, students have time to execute their research projects. Tutors are available for individual meetings, and students are encouraged to meet with their tutor for guidance on their projects.
  • The 3 ‘research weeks’ can additionally be used for guest lectures from experts on specific methods /techniques

13
(seminar)

  • Individual / group research on-going; additional tutor office hours

 

14
(seminar)

  • Individual / group research on-going; additional tutor office hours

 

 

(Reflection week)

 

15
(seminar)

  • Individual / group research on-going; additional tutor office hours

 

16
(seminar)

  • Deadline for students’ research report, which can be an article-length paper, a (photographic) exhibition, short documentary film, or other form (but consult with tutor first).
  • In-class experiment: in each seminar group, we will measure students’ social networks again and compare the results with their networks at the beginning of the course (see session 2).
  • Student presentations (part 1/2)
  • Hand in final research report by sending/handing it to tutor and fellow students in seminar group
  • Students will present their findings in their seminar groups, and are expected to read each other’s work beforehand. There will be time for critical (methodological) questions after each presentation.
  • Considering the group size, presentations will span two seminar sessions. Students are required to hand in their final report by session 16, however, and have to be present for all presentations.

17
(seminar)

  • Student presentations (part 2/2)
  • In-class experiment: in each seminar group, the results of the social network analysis of student relations are presented by the tutor (this serves as a ‘thank you’ to students, but hopefully also as an invitation to continued curiosity in research methodology for the future).