Designing Social Science Research

Oddbjørn Bukve

• Publisher: Palgrave Macmillan
• Year: 2019
• Print ISBN:
  9783030039783
• eBook ISBN:
  9783030039790
• Topic: Politics & International Relations
• Subtopic: Political History & Theory
• Index: Politics & International Relations

This book presents different research designs, their respective purposes and merits as well as their underlying assumptions. Research designs are characterised by a certain combination of knowledge aims and strategies for data production. An adequate design is the key to carrying out a successful research project. Nevertheless, the literature on design is scarce, compared to the literature on methods. This book clarifies the basic distinction between variable-oriented designs and case designs, and proceeds to integrated, comparative and intervention-oriented designs. A step-by-step guide to the design process and the choices to make is also included. The book's clear style makes it an excellent guide for master students and PhD students doing their first research exercises, while it is also useful for more experienced researchers who want to broaden their design repertoire and keep up to recent innovations in the field of research design.

© The Author(s) 2019

Oddbjørn BukveDesigning Social Science Researchhttps://doi.org/10.1007/978-3-030-03979-0_1

Begin Abstract

1. Introduction

Oddbjørn Bukve¹

(1)

Western Norway University of Applied Sciences, Sogndal, Norway

Oddbjørn Bukve

Email: [email protected]

End Abstract

A research design is a plan or an approach for carrying out a research project. The plan renders visible the logical structure of the project, the goals of the project, and the strategy to reach these goals. Thus, composing a research design includes two central elements: (1) defining the knowledge goals or purpose of the project and (2) deciding how to proceed to reach the goals. The distinguishing mark of a specific research design is the combination of knowledge goals and strategies to produce data. The knowledge goal has to do with what we want to find in the project, what we want to know. The strategy for data production must be planned in such a way that we will find data that are good and relevant enough to reach the knowledge goals of the project.

This book presents a number of research designs and shows how they are based on certain combinations of knowledge goals and strategies for data construction. It further presents the process of making a research design, step by step. The goal is to render the readers capable to make an expedient design for their project. I regard a clear and goal-oriented design to be the key to carrying out a successful project.

The starting point of the design is to identify and delimit the main knowledge goal of the project. To do so, we need to place the project goals in a theoretical context, at the same time clarifying the current status of our knowledge of the phenomenon we wish to research. In order to create a good knowledge goal, we need to identify a knowledge gap the project intends to fill or a problem that needs to be solved. The goals of a project can be formulated in a number of ways and vary from project to project. There can, however, be different types of purposes, which is how I wish to discuss knowledge goals in this book. Testing an established theory can be one goal; another is to precisely define and develop a theory about the phenomena and connections we are studying. Other goals can be to use established theory to understand and explain the phenomenon under investigation, or using it as a tool to find whether and why social interventions succeed or fail. The introductory question in a project should be related to one or more of these purposes, and a good design requires the project to be placed in the field of knowledge goals.

The other important element in a research design is how we construct data. The main division is between a case-centred and a variable-centred strategy for data construction. From a case-centred perspective we see the object for study as a whole or a system in which the parts are mutually dependent on each other and have to be studied in relation to each other. From a variable-centred perspective we decompose the object of study to its individual elements or variables, thus being able to isolate and study the effect of each element.

Creating a research design comprises more than choosing a method. The choice of method is primarily a question of technique, that is, of what techniques should be used to select, collect, and analyse data on the research phenomenon. If you take your starting point in the technical questions when planning your project, the tool will take control of the researcher rather than vice versa. The choice of method should therefore be part of a total design for the project. The method should not steer the knowledge goals but rather be a tool to achieve those goals. This book will show how different methods can be beneficial in different designs. Still, I do not treat the question of method in any detail. That is a task for the many books discussing research method.

Before going into this in more detail, I choose a somewhat different approach. I aim to show what difficulties we may get into if we do not sufficiently understand the role that design plays in the successful implementation of a research project. Through my experience as a thesis adviser I have found that there are some typical pitfalls that tend to make the realisation of research projects more challenging than necessary. These pitfalls will be described here before I go on to show how a research design may be developed. I believe that knowing the most common pitfalls is useful in order to avoid falling into them. For that reason, I will start with a review of some beginners’ problems often observed.

Hypotheses from Heaven

The first pitfall is the one I will call “hypotheses from heaven”. A number of books on research methods tell students to make precise hypotheses that can be tested through data collection. This makes some students start the planning of their project with only a rough presentation of the topic, then continuing by presenting a long “shopping list” containing various hypotheses. A hypothesis is an assertion of a possible or expected connection between two phenomena. Problems with such lists are that students often do not explain how they have arrived at the hypotheses and how they justify their choice and design of those hypotheses. Such an approach will soon lead you into trouble.

That said, there is nothing wrong with formulating precise hypotheses. They are necessary in many projects, although not in all. Still, hypotheses in a research project have to be either anchored in former research findings pointing towards such a hypothesis or one that deducts them from established theoretical perspectives. Before formulating hypotheses, you need to study the results of earlier research as well as what expectations can be derived from central theoretical perspectives in that particular field of research. With such a starting point, testing the hypotheses can support established knowledge or show that it will have to be revised. Thus, your research project will also be relevant to others. For this reason, the hypothesis formulation should not be made before you have proceeded some distance into the creation of a research design. In other words, wait with the hypotheses until you can anchor them and justify them in earlier research, and until you see that you really need them; maybe your project is of such a kind that you can use a more open approach.

Into the Fog Without a Compass

Just as common is the pitfall we can call “out into the fog without a compass”. Not all books on research method advise you to develop hypotheses and precise questions in advance. Some approaches to research tell you to proceed with an open and unbiased mind into the collection of data and only during the analysis link your findings to scientific concepts and theories. This piece of advice is not always wrong. Many interesting projects, development of new concepts, and new ways of understanding a phenomenon have been conducted in such a way. However, it is not a coincidence that these projects were usually developed by experienced researchers. This is a challenging approach that does not always give particularly interesting results. The approach should best be used when researching new and scarcely researched topics.

For master’s degree students or inexperienced researchers, confusing their own lack of knowledge in a research field with the belief that there is no other knowledge available is easily done. The danger then exists of repeating what has already been done. Another dilemma is that if we do not clarify what theoretical framework and concepts we find relevant to our project, we become blind to our own point of view. It is not easy to convincingly persuade others that we have seen something they have not seen if we do not understand where we are standing in relation to other researchers. There is also a practical side about clarifying our own point of departure before collecting data: the remaining research process will be much simpler. Making sense of a set of data collected without the compass of earlier research and established theories can create a lot of frustration and as a worst-case scenario result in a non-completed project.

The Cart Before the Horse

Pitfall number three may occur when at an early point in the process students decide that they want to use either a qualitative or a quantitative method in their project. The reason is often that they are more comfortable with the one or the other method. So far, it is not a problem. A potential problem arises when the student later starts working with the research topic and formulates research questions, and it becomes evident that the questions are not compatible with the chosen method. The research questions could, for instance, be meant to describe and understand a phenomenon in depth; but the student wants to use a quantitative method, which has its strength in explaining the relationship between various phenomena. Or the other way around: the student wishes to test hypotheses concerning a topic for which a quantitative method would be best suited, but has already decided to use a qualitative method.

In a well-constructed research project, the choice of method should ideally be adapted to and follow up the goals and questions the researcher has set for the project. The method is meant as a tool to find good answers to the problems the researcher wants to solve; it is not an ideal in itself. Choosing the method first can be compared to putting the cart before the horse (Gobo, 2008).

Picking Only the Sweet Fruits

Sometimes a student has a great interest in a particular theory. Mostly this is a general and wide theory with the zealous ambition of understanding and explaining a number of different phenomena, for instance, theories on organisational learning, institutional organisation theory, and theories on change management. The student wishes to use this particular theory to analyse a research topic. What is almost bound to happen is that the student will give a comprehensive and enthusiastic presentation of the theory, at the same time not being quite clear about what data will be useful to support the theory and what findings will weaken it. This makes the theory not suited to govern the data collection. The student will possibly choose and emphasise such data that support the theory while ignoring the data that do not. This means that the theory will not be subjected to critical testing.

The same could, of course, happen the other way around: The researcher is very sceptical to a particular theory, so much so that he or she is only open to data that can serve to weaken the theory. In both cases the researcher is only picking the fruits that are perceived as sweet, letting the others hang (Hancké, 2009). The best remedy is to be clear about what data may support or weaken the theory and then design the investigation in such a way that the chances of catching each type of data are equally good.

Unresearchable Problem Statements

Finally, you may run into problems in the course of the process because the way the problem statement is formulated makes it difficult to design a project that can give good answers to the research questions asked. A typical problem is a too wide and imprecise problem statement. It is not possible to include everything in a research project subject to limited time and resources. Good problem statements therefore need to be delimited to a degree that makes it possib...

Table of contents

• Cover
• Front Matter
• 1. Introduction
• 2. Scientific Knowledge and Practice
• 3. Social Science as Reconstruction of Social Phenomena
• 4. The Logic and Methodological Rules of Reconstruction
• 5. Design of Research Projects
• 6. Variable-Centred Designs
• 7. Case Designs
• 8. Integrated Designs
• 9. Comparative Designs
• 10. Intervention-Oriented Designs
• 11. The Design Process
• Back Matter