Research Design in Social Research – David de Vaus (book summary)
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Research Design and Research Methods (YRM20806)
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Wageningen University (WUR)
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Research Design in Social Research
This summary contains the book: Research Design in Social Research by David de Vaus: Chapters 1,2,3,4,5,7,8,10,11,13,14 and Zina O’Leary: Chapters 8 & 11. Used for the course: Research Design and Research Methods (YRM20806) at the Wageningen University.
Chapters: 1,2,3,4,5,7,8,10,11,13,14 (whole book except analysis chapters 6,9,12,15) chapter 8&11 f
October 4, 2019
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2019/2020
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david de vaus
research design in social reserach
zina oleary
research design and research methods
wageningen university
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Research Design and Research Methods – YRM20806
Research Design in Social Research – David de Vaus: Chapters 1,2,3,4,5,7,8,10,11,13,14
and Zina O’Leary: Chapters 8 & 11.
Chapter 1 – The context of design
Description and explanation
Two fundamental types of research questions:
1. What is going on? (descriptive research)
2. Why is it going on? (explanatory research)
Good description provokes the ‘why’ questions of explanatory research. The way in which researchers
develop research designs is fundamentally affected by whether the research question (RQ) is descriptive
or explanatory. It affects what information is collected.
Descriptive research Descriptions can be concrete or abstract:
Concrete description might describe the ethnic mix of a community, the changing age profile of a
population or the gender mic of a workplace.
Abstract description might be questions like ‘Is the level of social inequality increasing or declining’?,
‘How secular is society?’ or ‘How much property is there in this community?’.
Explanatory research This research focuses on the ‘why’. Why are things happening? Answering the
‘why’ questions involves developing causal explanations. Causal explanations argue that phenomenon Y
(e.g. income level) is affected by factor X (e.g. gender). We might argue that there is a direct effect of
gender on income, we might argue for a causal chain or we could posit a more complex model involving
a number of interrelated causal chains.
Prediction, correlation and causation
Confusing causation with correlation also confuses prediction with causation and prediction with
explanation. Where two events or characteristics are correlated we can predict one from the other. Good
prediction does not depend on causal relationships. Nor does the ability to predict accurately
demonstrate anything about causalities. Recognizing that causation is more than correlation highlight a
problem. While we can observe correlation, we cannot observe cause. We have to infer cause. One of
the fundamental purposes of research design in explanatory research is to avoid invalid inferences.
Deterministic and probabilistic concepts of causation
There are two ways of thinking about causes:
Deterministically causation where variable X is said to cause Y if, and only if, X invariably produces
Y. That is, when X is present then Y will ‘necessarily, inevitably and infallibly’ occur. This approach
seeks to establish causal laws (when water is heated to 100C it always boils) but in reality laws are
never this simple, they will always specify particular conditions under which the law operates.
- Example: the smoker who denies that tobacco causes cancer because he smokes heavily but has
not contracted cancer.
Probabilistic causation that we work at the level that a given factor increases (or decreases) the
probability of a particular outcome. Most causal thinking in social sciences is probabilistic.
- Example: being female increases the probability of working part time; race affects the probability
of having a high-status job.
Theory testing and theory construction
Attempts to answer the ‘why’ questions in social science are theories. These theories vary in their
complexity (how many variables and links), abstraction and scope. To understand the role of theory in
empirical research it is useful to distinguish between two different styles of research, theory building and
theory testing:
Theory building a process in which research begins with observations and uses inductive
reasoning to derive a theory from these observations. Because the theory is produced after
observations are made it is often called post factum theory or ex post facto theorizing. This form of
theory building entails asking whether the observation is a particular case of a more general factor, or
how the observation fits into a pattern or a story.
1
, Theory testing begins with a theory and uses theory
to guide which observations to make: it moves form
the general to the particular. The observations should
provide a test of the worth of the theory. Using
deductive reasoning to derive a set of propositions
form the theory does this. The propositions need to be
developed so that if the theory is true then certain
things should follow in the real world. We then assess
whether these predictions are correct, if they are
correct the theory is supported. If they do not hold up
then the theory needs to be either rejected or modified.
What is research design?
Social research needs a design or a structure before data collection or analysis can begin. A research
design is not just a work plan. A work plan details what has to be done to complete the project, but the
work plan will flow from the project’s research design. The function of a research design is to ensure that
the evidence obtained enables us to answer the initial question as clearly as possible. When designing
research, we need to ask: given this RQ (or theory), what type of evidence is needed to answer the
question in a convincing way?
Design versus method
Research design is different from the method by which data are collected. Research design is a logical
structure of the inquiry and a method a mode data collection.
Quantitative and qualitative research
Quantitative research social surveys and experiments are frequently viewed as prime examples,
they are evaluated against the strengths and weaknesses of statistical, quantitative research methods
and analysis.
Qualitative research case studies are often seen as prime examples, which adopts an
interpretative approach to data, studies ‘things’ within their context and considers the subjective
meanings that people bring to their situation.
The purpose of research design is to reduce the ambiguity of much research evidence. We can always
find some evidence consistent with almost any theory. However, we should be sceptical of the evidence,
and rather than seeking evidence that is consistent with our theory, we should seek evidence that
provides a compelling test of theory. There are two related strategies for doing this: eliminating rival
explanations of the evidence and deliberately seeking evidence that could disprove the theory.
Plausible rival hypothesis
A fundamental strategy of social research involves evaluating ‘plausible rival hypotheses’. We need to
examine and evaluate alternative ways of explaining a particular phenomenon. Good research design
will anticipate competing explanations before collecting data so that relevant information for evaluating
the relative merits of these competing explanations are obtained.
The fallacy of affirming the consequent To often people do not even think of the alternative
hypotheses and simply conclude that since the evidence is consistent with their theory then the theory is
true. This form of reasoning has the following logical structure:
- If A is true then B should follow;
- We observe B;
- Therefore, A is true.
Falsification: looking for evidence to disprove the theory
As well as evaluating and eliminating alternative explanations, we should rigorously evaluate our own
theory. Rather than asking ‘What evidence would constitute support for the theory?’, ask ‘What evidence
would convince me that the theory is wrong?’.
Non-falsifiable some theories are closed systems in which any evidence can be interpreted as support
for the theory.
2
,Summary chapter 1
In explanatory research the purpose is to develop and evaluate causal theories.
Research design is not related to any particular method of collecting data or any particular type of data
Research design refers to the structure of an enquiry: it is a logical matter rather than a logistical one
Design is a logical task undertaken to ensure that the evidence collected enables us to answer
questions or to test theories as unambiguously as possible
When designing research, it is essential that we identify the type of evidence required to answer the
RQ in a convincing way, research needs to be structured in such a way that the evidence also bears
on alternative rival explanations and enables us to identify which of the competing explanations is
most compelling empirically
We must not simply look for evidence that supports our favourite theory: we should also look for
evidence that has the potential to disprove our preferred explanations
Chapter 2 – Tools for research design
Before design
Social researcher must be clear about their RQ before developing a research design. The first question
to ask is ‘What question am I trying to answer?’. Specifying a question is more than identifying a topic.
Focusing descriptive RQ’s
To narrow the focus of descriptive research we need to specify the scope of what is to be described:
1. What is the scope of the core concepts? What is to be included in the concept? Until we specify what
we mean by our core concepts it is going to be impossible to begin the description.
2. What is the time frame for the description?
3. What is the geographical location?
4. How general is the description to be? (Do you want to be able to describe patterns for specific subgroups?)
5. What aspect of the topic are you interested in?
6. How abstract is your interest?
7. What is the unit of analysis? The unit of analysis is the ‘thing’ about which we collect information and
from which we draw conclusions. Often this is a person, but it may be ‘things’ such as organization, a
family as a whole, events, periods or places.
Focusing explanatory RQ’s
In framing explanatory questions, we need to further specify our focus. Explanatory research explores
causes and/or consequences of a phenomenon, so the RQ must be clear about the style of explanatory
research and identify which causes or consequences it will investigate. Before outlining some different
types of explanatory research, it is useful to introduce some terms:
Dependent variable the variable that is treated as the effect in the causal model: it is dependent on
the influence of some other factor. The dependent variable is also referred as the outcome variable
and in causal diagrams it is conventionally designated as the Y variable.
Independent variable the variable that is the presumed cause. It is also called the predictor
variable, the experimental variable or the explanatory variable and is designated in causal diagrams
as the X variable (as in education (X) income level (Y)).
Intervening variable these variables come between the independent variable and the dependent
variable in a causal chain. They are the means by which cause X produces effect Y. Intervening
variables are represented in causal diagrams by the symbol Z.
Education (X) Occupation (Z) Income level (Y)
Extraneous variables two variables can be correlated without being causally
related. The correlation may be due to the two factors being outcomes of a third
variable. This third variable is called an extraneous variable and is also symbolized
as Z in causal diagram.
Different types of explanatory research:
(Y)
Searching for causes or effect (least focused type): involves identifying the (X) ?
known
core phenomenon and then searching for causes of effect of this (see figure
(X)
(Y) ?
known
3
, above). Alternatively, we might focus on the consequences rather than the causes of changes (see
figure below).
Exploring a simple causal proposition a more focused RQ will specify a particular causal
proposition to evaluate. It might propose an impact of a particular factor or examine a specific
consequence.
More complex causal models such propositions are simplistic in that they do not spell out the
mechanisms by which two factors might be related. We might develop more complex models that
spell out some of the mechanisms in the causal chain. This fuller model then becomes the focus of
the research and provides the framework within which research design will be framed.
When clarifying a RQ it is helpful to draw diagrams. It is also helpful to ask four key questions:
1. What am I trying to explain? (i.e. what is the dependent variable)
2. What are the possible causes? (what are the independent variables)
3. Which causes will I explore?
4. What possible mechanisms connect the presumed causes to the presumed effects? (what are the
intervening variables)
Another way of framing RQ’s is to formulate different ways of understanding a phenomenon and then
compare which of these competing approaches best fits the facts.
Ideographic and nomothetic explanations
Nomothetic explanations partial explanations of a class of cases rather than a ‘full’ explanation of a
particular cases. They involve an examination of relatively few causal factors and a larger number of
cases. Would provide an understanding of the influence of a factor.
Idiographic explanations focus on particular cases and develop as complete an explanation of
each case as possible. They involve examining as many factors as possible that contribute to the
case including unique factors. Would provide a good understanding of the case.
Identifying plausible rival hypothesis
There are two main types of rival hypotheses that suggest ways of anticipating alternative explanations.
Theoretical and substantive rivals
Sources of alternative explanations:
The theoretical literature: broad approaches in a discipline can present different ways of viewing any
question. It could focus on different factors: personal attributes, economic, life course perspective etc.
Other researchers: previous research on the topic can be a rich source of competing explanations.
Practitioners, key informants, policy makers, advocates: insiders with practical knowledge of a field
can be invaluable.
Own experience, hunches and intuitions: do not ignore your own experience, your own intuitions and
hunches. In the end all explanations start with hunches that spring from individuals who have ideas
and observe things around them.
Technical/methodological rivals
If findings are likely to be due to poor measurement, then any theoretical interpretation of these results
will be unconvincing. Types of methodological rivals that will be examined:
Demand characteristics of the situation
Transient personal characteristics (mood, attention span, health)
Situational factors (anonymity, gender, age, class)
Sampling of items (Are the concepts well measured?)
Nature of the sample (Can we generalize from the sample?)
Lack of clarity of the instrument (Are the questions clear and unambiguous?)
Format of data collection (Are the results an artifact of the data collection method? Would different
patterns be found if a different method was used?)
Variation in the administration of the instrument in studies tracking change over time
Processing/analysis errors
Operationalization
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