Research-Methodology-for-Psychology-Englsih-Version-munotes

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PHILOSOPHY AND ETHICS OF
PSYCHOLOGICAL RESEARCH
Unit Structure
1.1 Introduction
1.2 Epistemological positions in psychological research: scientific
realism, logical positivism;Ockham‘s razor;
1.2.1 Epistemology:
1.2.2 Scientific realism:
1.2.3 Logi cal positivism:
1.2.4 Ockham‘s razor
1.3 Popper and Kuhn‘s contribution: theory dependence of observation;
understanding theory: components and connections – concepts,
constructs, variables and hypothesis; Duhem –Quine thesis; Quine‘s
critique of empirici sm
1.3.1 Theory dependence of observation
1.3.2 Understanding theory: components and connections – concepts,
constructs, variables and hypothesis
1.3.3 Duhem –Quine thesis
1.3.4 Quine‘s critique of empiricism
1.4 Ethical standards of psychological researc h: planning, conduction and
reporting research
1.5 Proposing and reporting quantitative research
1.6 References
1.1 INTRODUCTION Psychologists develop theories and conduct psychological research to
answer questions about behavior and mental processes; t hese answers can
impact individuals and society. The scientific method, a means to gain
knowledge, refers to the ways in which questions are asked and the logic
and methods used to gain answers. Two important characteristics of the
scientific method are an empirical approach and a skeptical attitude.
1.2 EPISTEMOLOGICAL POSITIONS IN PSYCHOLOGICAL RESEARCH: SCIENTIFIC
REALISM, LOGICAL POSITIVISM; OCKHAM’S
RAZOR Apart from any philosophical interest that we may have in science because
of its status and influe nce on our lives, science is important to philosophy
because it seems to offer answers to fundamental philosophical questions. munotes.in

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2 Philosophy And Ethics Of Psychological Research One such question is ‗how can we have knowledge as opposed to mere
belief or opinion?‘, and one very general answer to it is ‗fol low the
scientific method‘. So, for example, whatever any of us may believe,
rightly or wrongly, about whether smoking causes cancer or traffic fumes
cause asthma, a government will not act unless there is scientific evidence
supporting such beliefs (of co urse, they may still not act even when there
is evidence).
Similarly, in all the examples mentioned above, respect is accorded to the
views of scientists because their conclusions are supposed to have been
reached on the basis of proper methods of gatherin g and assessing
evidence, and hence are supposed to be justified.
1.2.1 Epistemology :
The branch of philosophy that inquires into knowledge and justification is
called epistemology. The central questions of epistemology include: what
is knowledge as oppos ed to mere belief?; can we be sure that we have any
knowledge?; what things do we in fact know?. The first of these is perhaps
the most fundamental epistemological question.
Each of us has many beliefs, some true and some false. If I believe
something that is, as a matter of fact, false (suppose, for example, that I
believe that the capital city of Australia is Sydney) then I cannot be said to
know it. In logical terminology we say a necessary condition, that is a
condition that must be satisfied, for someb ody knowing some proposition
is that the proposition is true.
In other words, if somebody knows some proposition then that proposition
is true. (The converse obviously does not hold; there are lots of
propositions that are true but which nobody knows, for example, there is a
true proposition about how many leaves there are on the tree outside my
window, but I presume nobody has bothered to find out what it is.) Where
someone believes something that turns out to be false (no matter how
plausible it seemed) t hen we would say that they thought they knew it but
that in fact they did not.
Suppose too that another necessary condition for somebody knowing some
proposition is that he or she believes that proposition. We now have two
necessary conditions for knowledg e; knowledge is at least true belief, but
is that enough? Consider the following example: suppose that I am very
prone to wishful thinking and every week I believe that my numbers will
come up on the lottery, and suppose that one particular week my numbers
do in fact come up; then I had a belief, that my numbers would come up,
and it was a true belief, but it was not knowledge because I had no
adequate reason to believe that my numbers would come up on that
particular week rather than on all the other weeks when I believed they
would come up, but when they did not. Hence, it may be the case that I
believe something, and that it is true, but that I do not know it. So it seems
that for something someone believes to count as knowledge, as well as
that belief be ing true, something else is required. My belief about the munotes.in

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3 Research Methodology For Psychology lottery in the example above did not count as knowledge because I lacked
an adequate reason to believe that I would win that week; we would say
that my belief was not justified. The traditional view in epistemology has
been that knowledge can only be claimed when we have an adequate
justification for our beliefs, in other words, knowledge is justified true
belief. Although recently this ‗tripartite‘ definition of knowledge has been
the subject of muc h criticism and debate, justification is still often
regarded as necessary for knowledge. This brings us to the issue of what
justification amounts to and, as suggested above, justification is often
thought to be provided by following scientific methods fo r testing or
arriving at our beliefs (the word science comes from the Latin word
scientia , which means knowledge).
1.2.2 Scientific realism :
Many of the entities postulated by modern science, such as genes, viruses,
atoms, black holes, and most forms of e lectromagnetic radiation, are
unobservable (at least with the unaided senses). So, whatever the scientific
method is and however scientific knowledge is justified, we can ask
whether we ought to believe what science tells us about reality beyond the
appear ances of things. Roughly speaking, scientific realism is the view
that we should believe in the unobservable objects postulated by our best
scientific theories. Of course, many of those who defend scientific realism
also defend the rationality of scientifi c theory change against sceptics and
relativists.
However, some ancient and modern critics of scientific realism have not
questioned the success or even the progress of scientific inquiry. Many
antirealists about scientific knowledge in the history of phil osophy are
happy to agree with realists that science is the paradigm of rational
inquiry, and that it has produced a cumulative growth of empirical
knowledge. However, antirealists of various kinds place limits on the
extent and nature of scientific knowle dge Hence, the issue of scientific
realism is more subtle than many of the polarised debates of science wars,
and it is important not to confuse the former with questions about the
rationality of science.
The disputes about scientific realism are closely related to those about
other kinds of realism in philosophy, some of which will be explained in
this chapter, but the reader – especially one with a good deal of scientific
knowledge – may already be feeling impatient. Isn‘t it just obvious that
plenty of unobservables described by scientific theories exist; after all,
don‘t scientists manipulate things like atoms and invisible radiation when
they design microchips and mobile phone networks? In fact, is it really
correct to describe atoms as unobservable? A fter all, don‘t we now see
photographs of crystal lattices made with microscopes that use electrons
instead of light to generate images? Is there really any room for reasonable
doubt that atoms exist when so many different parts of science describe
how the y behave and give rise to everything from the characteristic glow
of the gas in a neon light on a billboard, to the way that haemoglobin in
red blood cells absorbs oxygen in our lungs? Even if we decide that atoms munotes.in

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4 Philosophy And Ethics Of Psychological Research are now observable, the issue of principle returns when we ask about the
existence of the entities that supposedly make up atoms, and so on.
Furthermore, scientists of the past claimed to be manipulating and
observing theoretical entities that no longer feature in our best scientific
theories, so why should we have such faith that we have it right this time?
These and other arguments for and against scientific realism will be the
subject of the chapters that follow. First, in this chapter, I will explain the
background of the contemporary debate, a nd the different components of
scientific realism. We begin with the distinction between appearance and
reality.
1.2.3 Logical positivism :
The term ‗positivism‘ was coined by a French philosopher called Auguste
Comte (1798 –1857) who argued that societies pass through three stages –
namely the theological, the metaphysical and the scientific.In the
theological stage, people explain phenomena such as thunder, drought and
disease by invoking the actions of gods, spirits and magic. In the
metaphysical stage, t hey resort to unobservable forces, particles and so on.
The scientific stage is achieved when pretensions to explain why things
happen, or to know the nature of things in themselves, are renounced; the
proper goal of science is simply the prediction of phe nomena. He aimed to
complete the transition of European thought to the scientific stage by
advancing the scientific study of society and social relations (sociology),
and established a system of rituals celebrating scientists and science, to
replace the tr aditional calendar of Saint‘s Days and religious festivals.
Positivism has its roots in empiricism, especially in Hume‘s attempt to
separate the meaningful from meaningless
In general, positivists:
(a) emphasise verification/falsification;
(b) regard obse rvation/experience as the only source of knowledge
(empiricism);
(c) are anti -causation;
(d) are anti -theoretical entities;
(e) downplay explanation;
(f ) are, in general, anti -metaphysics.
Logical positivism was originally centred around a group of sc ientists,
mathematicians and philosophers called the Vienna Circle, which met in
the 1920s. Many of the Vienna Circle were Jewish and/or socialists. The
rise of fascism in Nazi Germany led to their dispersal to America and
elsewhere, where the ideas and pe rsonalities of logical positivism had a
great influence on the development of both science and philosophy. munotes.in

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5 Research Methodology For Psychology The difference between logical positivism and logical empiricism is a
matter of scholarly dispute. The most influential of those classified as
logica l positivists or empiricists include Moritz Schlick (1882 –1936), Carl
Hempel (1905 –1997), Carnap, Reichenbach (although he was in Berlin,
not Vienna), and Ayer (he visited the Circle and brought some of its ideas
to Britain). They all adopted the empiricis m of Hume and Mach and
Comte‘s aspiration for a fully scientific intellectual culture. What was new
about them was that they exploited the mathematical logic, recently
developed by Gottlob Frege (1848 –1925) and Russell among others, to
provide a framework within which theories could be precisely formulated.
The idea was that if the connections between ideas and associated
experiences could be made precise, then it would be possible to separate
meaningless metaphysical mumbo -jumbo from empirical science. is a
more fundamental principle of simplicity that is often claimed to be
essential to science, namely Occam‘s razor, which is roughly the
prescription not to invoke more entities in order to explain something than
is absolutely necessary. (This kind of simpl icity is called ontological
parsimony.) According to Ockham‘s razor, whenever we have two
competing hypotheses, then if all other considerations are equal, the
simpler of the two is to be preferred. Hume‘s empiricism means that he
thinks that, because the two hypotheses entail exactly the same thing with
respect to what we are able to observe, then all other considerations that
are worth worrying about are indeed equal.
1.2.4 Ockham’s razor :
The principle was, in fact, invoked before Ockham by Durandus of S aint-
Pourçain, a French Dominican theologian and philosopher of dubious
orthodoxy, who used it to explain that abstraction is the apprehension of
some real entity, such as an Aristotelian cognitive species, an active
intellect, or a disposition, all of whi ch he spurned as unnecessary.
Likewise, in science, Nicole d‘Oresme, a 14th -century French physicist,
invoked the law of economy, as did Galileo later, in defending the
simplest hypothesis of the heavens. Other later scientists stated similar
simplifying l aws and principles.
Ockham, however, mentioned the principle so frequently and employed it
so sharply that it was called ―Occam‘s razor‖ (also spelled Ockham‘s
razor). He used it, for instance, to dispense with relations, which he held to
be nothing distin ct from their foundation in things; with efficient causality,
which he tended to view merely as regular succession; with motion, which
is merely the reappearance of a thing in a different place; with
psychological powers distinct for each mode of sense; an d with the
presence of ideas in the mind of the Creator, which are merely the
creatures themselves.
In science, Occam‘s razor is used as a heuristic to guide scientists in
developing theoretical models rather than as an arbiter between published
models. In physics, parsimony was an important heuristic in Albert
Einstein‘s formulation of special relativity, [45][46] in the development
and application of the principle of least action by Pierre Louis munotes.in

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6 Philosophy And Ethics Of Psychological Research Maupertuis and Leonhard Euler, and in the development of quan tum
mechanics by Max Planck, Werner Heisenberg and Louis de Broglie.
When scientists use the idea of parsimony, it has meaning only in a very
specific context of inquiry. Several background assumptions are required
for parsimony to connect with plausibili ty in a particular research
problem. The reasonableness of parsimony in one research context may
have nothing to do with its reasonableness in another. It is a mistake to
think that there is a single global principle that spans diverse subject
matter.
1.3 POPPER AND KUHN’S CONTRIBUTION: THEORY DEPENDENCE OF OBSERVATION; UNDERSTANDING
THEORY: COMPONENTS AND CONNECTIONS –
CONCEPTS, CONSTRUCTS, VARIABLES AND
HYPOTHESIS; DUHEM –QUINE THESIS; QUINE’S
CRITIQUE OF EMPIRICISM Karl Popper had a considerable influenc e on philosophy of science
during the twentieth century and many scientists took up his ideas. As a
result, he was made a member of the Royal Society of London, which is
one of the most prestigious scientific associations. In fact, Popper‘s
falsificationis m is probably now more popular among scientists than it is
among philosophers. Popper also played an important role in the
intellectual critique of Marxism, and his books The Poverty of Historicism
and The Open Society and Its Enemies are still widely read by political
theorists today. His interest in philosophy of science began with the search
for a demarcation between science and pseudo -science. He tried to work
out what the difference was between theories he greatly admired in
physics, and theories he th ought were unscientific in psychology and
sociology, and soon came to the conclusion that part of the reason why
people erroneously thought that mere pseudo -sciences were scientific was
that they had a mistaken view about what made physics scientific.
Popp er‘s solution to the problem of induction is simply to argue that it
does not show that scientific knowledge is not justified, because science
does not depend on induction at all. Popper pointed out that there is a
logical asymmetry between confirmation an d falsification of a universal
generalisation. The problem of induction arises because no matter how
many positive instances of a generalisation are observed it is still possible
that the next instance will falsify it. However, if we take a generalisation
such as all swans are white, then we need only observe one swan that is
not white to falsify this hypothesis.
Popper argued that science is fundamentally about falsifying rather than
confirming theories, and so he thought that science could proceed without
induction because the inference from a falsifying instance to the falsity of
a theory is purely deductive. (Hence, his theory of scientific method is
called falsificationism .) munotes.in

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7 Research Methodology For Psychology Kuhn was a physicist who became interested in the history of science and
especi ally the Copernican revolution. The standard view that he found
presented in textbooks and in historical and philosophical works, was that
the Copernican revolution, and especially the argument between Galileo
and the Catholic Church, was a battle between reason and experiment on
the one hand, and superstition and religious dogma on the other. Many
historians and scientists suggested that Galileo and others had found
experimental data that were simply inconsistent with the Aristotelian view
of the cosmos. K uhn realised that the situation was considerably more
complex, and he argued that the history of this and other revolutions in
science was incompatible with the usual inductivist and falsificationist
accounts of the scientific method. Kuhn‘s book The Struc ture of Scientific
Revolutions (1962) offered a radically different way of thinking about
scientific methodology and knowledge, and changed the practice of
history of science. His philosophy of science has influenced academia
from literary theory to manage ment science, and he seems single -handedly
to have caused the widespread use of the word ‗paradigm‘ .
According to Kuhn, the evaluation of theories depends on local historical
circumstances, and his analysis of the relationship between theory and
observati on suggests that theories infect data to such an extent that no way
of gathering of observations can ever be theoryneutral and objective.
Hence, the degree of confirmation an experiment gives to a hypothesis is
not objective, and there is no single logic o f theory testing that can be used
to determine which theory is most justified by the evidence. He thinks,
instead, that scientists‘ values help determine, not just how individual
scientists develop new theories, but also which theories the scientific
commu nity as a whole regards as justified
1.3.1 Theory dependence of observation :
The idea that observation is theory dependent is central to many debates in
the philosophy of science. This concept is a reaction to the idea that
disputes about the way the world is can be easily and simply resolved by
simply ‗looking at the facts‘, or performing some sort of experiment or
observation. The problem with this answer is that there is no ‗neutral‘
vantage point from which such facts can be gathered or interpreted.
Rather, empirical evidence is always interpreted within the context of
one‘s preexisting ideas, conceptions, and expectations, which can often
have a dramatic effect on how observations are understood or what they
are taken to mean.
Historians of science have given many examples of instances where
proponents of rival theories have interpreted the same empirical evidence
in very different ways, in accordance with their theoretical commitments.
An interesting illustrative case can be found in a popular drawing c alled
the ‗duck -rabbit‘, a sketch which can be interpreted as either a drawing of
a duck or of a rabbit, depending on the ‗theory‘ one applies in interpreting
the pattern of lines. While in this particular case both interpretations are
equally ‗correct‘, i n many cases scientific and philosophical disputes, munotes.in

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8 Philosophy And Ethics Of Psychological Research however, it is often unclear whether one, both, or none of the differing
interpretations of the relevant facts are correct.
Another problem with observation is that there are always far too many
empirical facts for us to consider all of them. One must have some way of
selecting which facts are ‗relevant‘ and which are not, an activity which
naturally requires the use of some theory, a theory which in turn may be
widely disputed. For example, scientists do not spent their time counting
the number of blades of grass on every lawn, even though this would
result in the collection of more facts, because we have reason to think that
this fact has no real importance or significance. If, however, we believed
that t he number of blades of grass on a field was a form of communication
from an extraterrestrial race, or a sign from some divine being, then our
attitude towards the significance and meaning of the very same facts
would doubtless be very different. This dispu te could not simply be
resolved by ‗looking at the facts‘, because which facts we regard as
relevant would depend upon which theory we accepted.
How to resolve such problems has been the subject of considerable
philosophical attention, and remains an ongoi ng problem for any attempt
to provide a comprehensive philosophical underpinning for scientific
inquiry.
1.3.2 Understanding theory: components and connections – concepts,
constructs, variables and hypothesis :
A theory is a method we use to give us underst anding. One of the major
purposes of a theory is to provide an answer to the question ‗ why? ‘.
Asking, ‗ why? ‘, to increase your knowledge of a subject area and realign
your thoughts and opinions is an essential skill for anybody who wants to
learn and deve lop.
‗Why‘ is one of the very first questions that children ask:
 “Can you get ready for bed now?” … “Oh why?”
 “Why is snow cold?”
 “Why do I have to go to school tomorrow?”
 “Why is the sky blue?”
Questions like these, from children, can be endless. Often fi nding or
providing suitable explanations can be exhausting and frustrating –
perhaps we resort to saying, ― Well it just is! ‖ At the basis of such
questions however, are a child‘s first attempts to understand the world
around them, and develop their own th eories of why things are the way
they are.
Defining ‗theory‘, therefore, has to take into account the ‗why?‘ question,
but a theory is deeper than that. The points below go some way to helping
with a definition. munotes.in

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9 Research Methodology For Psychology  A theory is an attempt to explain why and s o to provide
understanding.
 A theory is not just ‗any‘ explanation - a theory comes into being
when a series of ideas come to be held and accepted by a wider
community of people.
 A theory is not necessarily factually based – how we understand and
provide explanations arises from our cultural background and how we
view the world.
Components : One lesson is that the reason a ―good‖ theory should be
testable, be coherent, be economical, be generalizable, and explain known
findings is that all of these charact eristics serve the primary function of a
theory –to be generative of new ideas and new discoveries.
The components of theory are concepts (ideally well defined)
and principles .
A concept is a symbolic representation of an actual thing - tree, chair,
table, computer, distance, etc. Concept is a world that expresses an
abstraction formed by generalizations from particulars e.g., weight,
achievement
Construct is the word for concepts with no physical referent - democracy,
learning, freedom, etc. Language enable s conceptualization. Construct has
the added meaning of having been deliberately and consciously invented
or adopted for a special scientific purpose
A principle expresses the relationship between two or more concepts or
constructs.
In the process of theor y development, one derives principles based on
oneÕs examining/questioning how things/concepts are related.
Concepts and principles serve two important functions :
1) They help us to understand or explain what is going on around us.
2) They help us predic t future events (Can be causal or correlational)
A Problem is an interrogative sentence or statement about the relationship
between tow or more variables. Do teachers‘ comments cause
improvement in student performance?
A research problem is a specific issu e, difficulty, contradiction, or gap in
knowledge that you will aim to address in your research. You might look
for practical problems aimed at contributing to change, or theoretical
problems aimed at expanding knowledge.
Bear in mind that some research wi ll do both of these things, but usually
the research problem focuses on one or the other. The type of research
problem you choose depends on your broad topic of interest and the type
of research you want to do. munotes.in

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10 Philosophy And Ethics Of Psychological Research Why is the research problem important? Your topic is interesting and you
have lots to say about it, but this isn‘t a strong enough basis for academic
research. Without a well -defined research problem, you are likely to end
up with an unfocused and unmanageable project.
You might end up repeating wha t other people have already said, trying to
say too much, or doing research without a clear purpose and justification.
You need a problem in order to do research that contributes new and
relevant insights.
Whether you‘re planning your thesis, starting a re search paper or writing a
research proposal, the research problem is the first step towards knowing
exactly what you‘ll do and why.
In research, variables are any characteristics that can take on different
values, such as height, age, temperature, or test scores.
Researchers often manipulate or measure independent and dependent
variables in studies to test cause -and-effect relationships.
The independent variable is the cause. Its value is independent of other
variables in your study.
The dependent variable is the effect. Its value depends on changes in the
independent variable.
Example:
You design a study to test whether changes in room temperature have an
effect on math test scores.
Your independent variable is the temperature of the room. You vary the
room temperature by making it cooler for half the participants, and
warmer for the other half.
Your dependent variable is math test scores. You measure the math skills
of all participants using a standardized test and check whether they differ
based on room t emperature.
A Hypothesis is a conjectural or declarative sentence or statement of the
relation between two or more variables. Teachers‘ reinforcement would
have significant impact on students performance.
As a researcher, we never know the outcome prior to the research work
but we will have certain assumptions on how the end results will be. Based
on our hunch and curiosity, we will test it by collecting information that
will enable us to conclude whether our assumptions are right. hypothesis
has several fu nctions:
(a) Enhance the objectivity and purpose of a research work;
(b) Provide a research with focus and tells a researcher the specific scope
of a research problem to investigate; munotes.in

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11 Research Methodology For Psychology (c) Help a researcher in prioritising data collection, hence providing focus
on the study; and
(d) Enable the formulation of theory for a researcher to specifically
conclude what is true and what is not.
Generally, there is only one type of hypothesis, that is, research
hypothesis. Research hypothesis forms the basis of inve stigation for a
researcher. However,recent conventions in the scientific field and inquiries
stated that hypothesis can be classified into two main categories ÀÛÝ
research hypothesis and alternate hypothesis. Alternate hypothesis is a
convention among the scientific community. The main function of an
alternate hypothesis is to explicitly specify the relationship that will be
considered true in case the research hypothesis proves to be wrong. We
can see that in a way, alternate hypothesis is the opposite of research
hypothesis. As you may come across a null hypothesis, hypothesis of no
differences, these are all formulated as alternate hypothesis.
1.3.3 Duhem –Quine thesis :
The Quine -Duhem thesis is a form of the thesis of the underdetermination
of theory by e mpirical evidence. The basic problem is that individual
theoretical claims are unable to be confirmed or falsified on their own, in
isolation from surrounding hypotheses. For this reason, the acceptance or
rejection of a theoretical claim is underdetermi ned by observation. The
thesis can be interpreted in a more radical form that tends to be associated
with the epistemic holism of Willard V. O. Quine or in a more restricted
form associated with Pierre Duhem. It is primarily an epistemic thesis
about the relation between evidence and theory, though in Quine‘s case it
also has semantic overtones connected with his rejection of the analytic -
synthetic distinction.
Although a bundle of hypotheses (i.e. a hypothesis and its background
assumptions) as a whole can be tested against the empirical world and be
falsified if it fails the test, the Duhem –Quine thesis says it is impossible to
isolate a single hypothesis in the bundle. One solution to the dilemma thus
facing scientists is that when we have rational reas ons to accept the
background assumptions as true (e.g. scientific theories via evidence) we
will have rational —albeit nonconclusive —reasons for thinking that the
theory tested is probably wrong if the empirical test fails.
As popular as the Duhem –Quine the sis may be in philosophy of science,
in reality Pierre Duhem and Willard Van Orman Quine stated very
different theses. Duhem believed that only in the field of physics can a
single individual hypothesis not be isolated for testing. He says in no
uncertain terms that experimental theory in physics is not the same as in
fields like physiology and certain branches of chemistry. Also, Duhem‘s
conception of ―theoretical group‖ has its limits, since he states that not all
concepts are connected to each other logi cally. He did not include at all a
priori disciplines such as logic and mathematics within the theoretical
groups in physics, since they cannot be tested. munotes.in

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12 Philosophy And Ethics Of Psychological Research Quine, on the other hand, in ―Two Dogmas of Empiricism‖, presents a
much stronger version of underdet ermination in science. His theoretical
group embraces all of human knowledge, including mathematics and
logic. He contemplated the entirety of human knowledge as being one unit
of empirical significance. Hence all our knowledge, for Quine, would be
epistem ologically no different from ancient Greek gods, which were
posited in order to account for experience.
Quine even believed that logic and mathematics can also be revised in
light of experience, and presented quantum logic as evidence for this.
Years later he retracted this position; in his book Philosophy of Logic, he
said that to revise logic would be essentially ―changing the subject‖. In
classic logic, connectives are defined according to truth values. The
connectives in a multi -valued logic, however, h ave a different meaning
than those of classic logic. As for quantum logic, it is not even a logic
based on truth values, so the logical connectives lose the original meaning
of classic logic. Quine also notes that deviant logics usually lack the
simplicity of classic logic, and are not so fruitful.
1.3.4 Quine’s critique of empiricism :
In his seminal paper ―Two Dogmas of Empiricism‖ (1951), Quine
rejected, as what he considered the first dogma, the idea that there is a
sharp division between logic and empir ical science. He argued, in a vein
reminiscent of the later Wittgenstein, that there is nothing in the logical
structure of a language that is inherently immune to change, given
appropriate empirical circumstances. Just as the theory of special relativity
undermines the fundamental idea that events simultaneous to one observer
are simultaneous to all observers, so other changes in what human beings
know can alter even their most basic and ingrained inferential habits.
The other dogma of empiricism, accordin g to Quine, is that associated
with each scientific or empirical sentence is a determinate set of
circumstances whose experience by an observer would count as
disconfirming evidence for the sentence in question. Quine argued that the
evidentiary links betw een science and experience are not, in this sense,
―one to one.‖ The true structure of science is better compared to a web, in
which there are interlinking chains of support for any single part. Thus, it
is never clear what sentences are disconfirmed by ―r ecalcitrant
experience‖; any given sentence may be retained, provided appropriate
adjustments are made elsewhere. Similar views were expressed by the
American philosopher Wilfrid Sellars (1912 –89), who rejected what he
called the ―myth of the given‖: the i dea that in observation, whether of the
world or of the mind, any truths or facts are transparently present. The
same idea figured prominently in the deconstruction of the ―metaphysics
of presence‖ undertaken by the French philosopher and literary theorist
Jacques Derrida (1930 –2004).
If language has no fixed logical properties and no simple relationship to
experience, it may seem close to having no determinate meaning at all.
This was in fact the conclusion Quine drew. He argued that, since there munotes.in

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13 Research Methodology For Psychology are no co herent criteria for determining when two words have the same
meaning, the very notion of meaning is philosophically suspect. He further
justified this pessimism by means of a thought experiment concerning
―radical translation‖: a linguist is faced with the task of translating a
completely alien language without relying on collateral information from
bilinguals or other informants. The method of the translator must be to
correlate dispositions to verbal behaviour with events in the alien‘s
environment, until eventually enough structure can be discerned to impose
a grammar and a lexicon. But the inevitable upshot of the exercise is
indeterminacy. Any two such linguists may construct ―translation
manuals‖ that account for all the evidence equally well but that ―stand in
no sort of equivalence, however loose.‖ This is not because there is some
determinate meaning —a unique content belonging to the words —that one
or the other or both translators failed to discover. It is because the notion
of determinate meaning si mply does not apply. There is, as Quine said, no
―fact of the matter‖ regarding what the words mean.
1.4 ETHICAL STANDARDS OF PSYCHOLOGICAL RESEARCH: PLANNING, CONDUCTION AND
REPORTING RESEARCH Researchers must consider ethical issues before they begin a research
project. Ethical problems can be avoided only by planning carefully and
consulting with appropriate individuals and groups prior to doing the
research. The failure to conduct research in an ethical manner undermines
the entire scientifi c process, impedes the advancement of knowledge, and
erodes the public‘s respect for scientifi c and academic communities (see
Figure 3.2). It can also lead to signify cant legal and fi nancial penalties
for individuals and institutions. An important step that resea rchers must
take as they begin to do psychological research is to gain institutional
approval.
Prior to conducting any study, the proposed research must be reviewed to
determine if it meets ethical standards. Institutional Review Boards (IRBs)
review psych ological research to protect the rights and welfare of human
participants. Institutional Animal Care and Use Committees (IACUCs)
review research conducted with animals to ensure that animals are treated
humanely.
 Risk/benefit ratio: A subjective evaluation of the risks and benefits of
a research project is used to determine whether the research should be
conducted.
 Potential risks in psychological research include risk of physical
injury, social injury, and mental or emotional stress.
 To protect participant s from social risks, information they provide
should be anonymous, or if that is not possible, the confidentiality of
their information should be maintained.
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14 Philosophy And Ethics Of Psychological Research Informed Consent:
 Researchers and participants enter into a social contract, often using
an infor med consent procedure.
 Researchers are ethically obligated to describe the research
procedures clearly, and answer any questions participants have about
the research.
 Research participants must be allowed to withdraw their consent at
any time without penal ties.
 Individuals must not be pressured to participate in research.
Deception in psychological research:
 Deception in psychological research occurs when researchers
withhold information or intentionally misinform participants about the
research. By its nat ure, deception violates the ethical principle of
informed consent.
 Deception is considered a necessary research strategy in some
psychological research.
Debriefing:
 Debriefing informs participants about the nature of the research and
their role in the stud y and educates them about the research process.
The prime goal of debriefing is to have individuals feel good about
their participation
 Researchers are ethically obligated to explain to participants their use
of deception as soon as is feasible.
 Debriefing allows researchers to learn how participants viewed the
procedures, allows potential insights into the nature of the research
findings, and provides ideas for future research.
Research with animal:
 Animals are used in research to gain knowledge that will benefit
humans, for example, by helping to cure diseases.
 Researchers are ethically obligated to acquire, care for, use, and
dispose of animals in compliance with current federal, state, and local
laws and regulations, and with professional standards.
 The use of animals in research involves complex issues and is the
subject of much debate.


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15 Research Methodology For Psychology Reporting of psychological research:
 Investigators attempt to communicate their research findings in peer
reviewed scientific journals, and the APA Code of Ethics prov ides
guidelines for this process.
 Decisions about who should receive publication credit are based on
the scholarly importance of the contribution.
 Ethical reporting of research requires recognizing the work of others
by using proper citations and reference s; failure to do so may result in
plagiarism
1.5 PROPOSING AND REPORTING QUANTITATIVE RESEARCH The purpose of the research proposal (it‘s job, so to speak) is to convince
your research supervisor, committee or university that your research is
suitable (fo r the requirements of the degree program) and manageable
(given the time and resource constraints you will face).
The most important word here is ―convince‖ – in other words, your
research proposal needs to sell your research idea (to whoever is going to
approve it). If it doesn‘t convince them (of its suitability and
manageability), you‘ll need to revise and resubmit. This will cost you
valuable time, which will either delay the start of your research or eat into
its time allowance (which is bad news).
A good dissertation or thesis proposal needs to cover the ―what‖, the
―why‖ and the ―how‖ of the research. Let‘s look at each of these in a little
more detail:
WHAT – Your research topic
Your proposal needs to clearly articulate your research topic. This nee ds to
be specific and unambiguous. Your research topic should make it clear
exactly what you plan to research and in what context. Here‘s an example:
Topic: An investigation into the factors which impact female Generation
Y consumer‘s likelihood to promote a specific makeup brand to their
peers: a British context
What‘s being investigated – factors that make people promote a brand of
makeup
Who it involves – female Gen Y consumers
In what context – the United Kingdom
So, make sure that your research proposa l provides a detailed explanation
of your research topic. It should go without saying, but don‘t start writing
your proposal until you have a crystal -clear topic in mind, or you‘ll end up
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16 Philosophy And Ethics Of Psychological Research WHY – Your justification
As we touched on earlier, it‘s not good enough to simply propose a
research topic – you need to justify why your topic is original. In other
words, what makes it unique? What gap in the current literature does it
fill? If it‘s simply a rehash of the existing res earch, it‘s probably not going
to get approval – it needs to be fresh.
But, originality alone is not enough. Once you‘ve ticked that box, you also
need to justify why your proposed topic is important. In other words, what
value will it add to the world if you manage to find answers to your
research questions?
For example, let‘s look at the sample research topic we mentioned earlier
(factors impacting brand advocacy). In this case, if the research could
uncover relevant factors, these findings would be very useful to marketers
in the cosmetics industry, and would, therefore, have commercial value.
That is a clear justification for the research.
So, when you‘re crafting your research proposal, remember that it‘s not
enough for a topic to simply be unique. It needs to be useful and value -
creating – and you need to convey that value in your proposal. If you‘re
struggling to find a research topic that makes the cut, watch our video
covering how to find a research topic.
HOW – Your methodology
It‘s all good and we ll to have a great topic that‘s original and important,
but you‘re not going to convince anyone to approve it without discussing
the practicalities – in other words:
How will you undertake your research?
Is your research design appropriate for your topic?
Is your plan manageable given your constraints (time, money, expertise)?
While it‘s generally not expected that you‘ll have a fully fleshed out
research strategy at the proposal stage, you will need to provide a high -
level view of your research methodolog y and some key design decisions.
Here are some important questions you‘ll need to address in your
proposal:
Will you take a qualitative or quantitative approach?
Will your design be cross -sectional or longitudinal?
How will you collect your data (intervi ews, surveys, etc)?
How will you analyse your data (e.g. statistical analysis, qualitative data
analysis, etc)?
So, make sure you give some thought to the practicalities of your research
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17 Research Methodology For Psychology you start writing up your proposal. The video below provides a good
introduction to methodology.
Reporting quantitative research :
A quantitative analysis can give people the necessary information to make
decisions about policy and planning for a progr am or organization. A good
quantitative analysis leaves no questions about the quality of data and the
authority of the conclusions. Whether in school completing a project or at
the highest levels of government evaluating programs, knowing how to
write a q uality quantitative analysis is helpful. A quantitative analysis uses
hard data, such as survey results, and generally requires the use of
computer spreadsheet applications and statistical know -how.
Step 1 :
Explain why the report is being written in the in troduction. Point out the
need that is being filled and describe any prior research that has been
conducted in the same field. The introduction should also say what future
research should be done to thoroughly answer the questions you set out to
research. You should also state for whom the report is being prepared.
Step 2 :
Describe the methods used in collecting data for the report. Discuss how
the data was collected. If a survey was used to collect data, tell the reader
how it was designed. You should let the reader know if a survey pilot test
was distributed first. Detail the target population, or the group of people
being studied. Provide the sample size, or the number of people surveyed.
Tell the reader if the sample was representative of the target popu lation,
and explain whether you collected enough surveys. Break down the data
by gender, race, age and any other pertinent subcategory. Tell the reader
about any problems with data collection, including any biases in the
survey, missing results or odd resp onses from people surveyed.
Step 3 :
Create graphs showing visual representations of the results. You can use
bar graphs, line graphs or pie charts depending to convey the data. Only
write about the pertinent findings, or the ones you think matter most, in
the body of the report. Any other results can be attached in the appendices
at the end of the report. The raw data, along with copies of a blank survey
should be in the appendices as well. The reader can refer to all the data to
inform his own opinions abo ut the findings.
Step 4 :
Write conclusions after evaluating all the data. The conclusion can include
an action item for the reader to accomplish. It can also advise that more
research needs to be done before any solid conclusions can be made. Only
conclusi ons that can be made based on the findings should be included in
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18 Philosophy And Ethics Of Psychological Research Step 5 :
Write an executive summary to attach at the beginning of the report.
Executive summaries are quick one to two page recaps of what is in the
report. They include shorter ve rsions of the introductions, methods,
findings and conclusions. Executive summaries serve to allow readers to
quickly understand what is said in the report.
1.6 REFERENCES 1. Shaughnessy, J. J., Zechmeister, E. B. &Zechmeister, J. (2012).
Research methods in psychology . (9th ed..). NY: McGraw Hill.
2. Elmes, D. G. (2011). Research Methods in Psychology (9thed.).
Wadsworth Publishing.
3. Goodwin, J. (2009). Research in Psychology: Methods in Design
(6thed.). Wiley.
4. McBurney, D. H. (2009). Research methods . (8th Ed.). Wadsworth
Publishing.



*****











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19
2
RESEARCH SETTINGS AND MEHTODS
OF DATA COLLECTION
Unit Structure
2.1 Introduction
2. 2 Observation and Interview method
2.2.1 Observation
2.2.2 Interviews
2.3 Questionnaire
2.4 Survey research
2.5 Other non -experimental methods
2.6 References
2.1 INTRODUCTION Collecting data involves gathering the information obtained from your
measures to help test your research question. As such, data collection
methods are specific to your project and will be different from those used
by other researchers. The types of measures used (e.g. questionnaires,
online experiments, etc.) and who you will be recruiting as your
participants (e.g. local students, international individuals, clinical groups,
etc.) will determine your data collection methods. For instance, y ou may
decide to administer an online survey via Qualtrics or conduct experiments
online using Inquisit or OpenSesame.
Regardless of the method of research, data collection will be necessary.
The method of data collection selected will primarily depend on the type
of information the researcher needs for their study; however, other factors,
such as time, resources, and even ethical considerations can influence the
selection of a data collection method. All of these factors need to be
considered when selecti ng a data collection method because each method
has unique strengths and weaknesses. We will discuss the uses and
assessment of the most common data collection methods: observation,
surveys, archival data, and tests.
Data collection is the procedure of col lecting, measuring and analyzing
accurate insights for research using standard validated techniques. A
researcher can evaluate their hypothesis on the basis of collected data. In
most cases, data collection is the primary and most important step for
resear ch, irrespective of the field of research. The approach of data
collection is different for different fields of study, depending on the
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20 Research Settings And Mehtods Of Data Collection The most critical objective of data collection is ensuring that information -
rich and reliable data is collected for statistical analysis so that data -driven
decisions can be made for research.
The research setting can be seen as the physical, social, and cultural site in
which the researcher conducts the study. In qualitative research, the focus
is mainly on meaning -making, and the researcher studies the participants
in their natural setting.
The environment within which studies are run has important consequences
for experimental design, the type of data that can be collected and the
interpretation o f results. so, for example running a study in an
experimental laboratory may allow you to control variables in a way you
cannot do in field work, and the results may be criticised for not reflecting
real life. It is often important to conduct complementary studies in various
research settings in order to build arguments for the generalisability of
findings.
Data collection techniques include interviews, observations (direct and
participant), questionnaires, and relevant documents. The use of multiple
data c ollection techniques and sources strengthens the credibility of
outcomes and enables different interpretations and meanings to be
included in data analysis. This is known as triangulation (Flick, 2014).
How often the data is collected:
It relies on particu lar event happenings or even every movement of the
subjects life. Therefore often researchers use sampling to gather
information through various observation. It is much needed to make sure
that the sample of the data is representative of the subjects overa ll
behaviour. Students and new researchers face difficulties to make a
decision Survey questionnaire development for dissertation help will give
you a confident of your thesis data in safe hands.
A representative sample obtained through:
Time Sampling: Tak ing a sample from different interval of time randomly,
this type is entirely on the time interval report.
Situation Sampling:situation sampling is taking down the readings based
on the movement of the subject. This type doesn’t include any time
interval th e information is taken when there is a need.
2.2 OBSERVATION AND INTERVIEW METHOD 2.2.1 Observation :
The observational method involves the watching and recording of a
specific behavior of participants. In general, observational studies have the
strength o f allowing the researcher to see for themselves how people
behave. However, observations may require more time and man -power
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21 Research Methodology For Psychology participants. Researchers may spend significant time wait ing to observe a
behavior, or the behavior may never occur during observation. It is
important to remember that people tend to change their behavior when
they know they are being watched (known as the Hawthorne effect).
Observations may be done in a natura list setting to reduce the likelihood
of the Hawthorne effect. During naturalistic observations, the participants
are in their natural environment and are usually unaware that they are
being observed. For example, observing students participating in their
class would be a naturalist observation. The downside of a naturalistic
setting is that the research doesn’t have control over the environment.
Imagine that the researcher goes to the classroom to observe those
students, and there is a substitute teacher. The change in instructor that day
could impact student behavior and skew the data.
If controlling the environment is a concern, a laboratory setting may be a
better choice. In the laboratory environment, the researcher can manage
confounding factors or dis tractions that might impact the participants’
behavior. Of course, there are expenses associated with maintaining a
laboratory setting, increasing the cost of the study, that would not be
associated with naturalist observations. And, again, the Hawthorne e ffect
may impact behavior.
Observation allows researchers to experience a specific aspect of social
life and get a firsthand look at a trend, institution, or behavior. Participant
observation involves the researcher joining a sample of individuals
without interfering with that group’s normal activities in order to
document their routine behavior or observe them in a natural context.
Often researchers in observational studies will try to blend in seamlessly
with the sample group to avoid compromising the res ults of their
observations.
Observational research is a type of descriptive research that differs from
most other forms of data gathering in that the researcher’s goal is not to
manipulate the variables being observed. While participants may or may
not be aware of the researchers’ presence, the researchers do not try to
control variables (as in an experiment), or ask participants to respond to
direct questions (as in an interview or survey based study). Instead, the
participants are simply observed in a nat ural setting, defined as a place in
which behavior ordinarily occurs, rather than a place that has been
arranged specifically for the purpose of observing the behavior. Unlike
correlational and experimental research which use quantitative data,
observation al studies tend to use qualitative data.
For example, social psychologists Roger Barker and Herbert Wright
studied how a sample of children interacted with their daily environments.
They observed the children go to school, play with friends, and complete
daily chores, and learned a great deal about how children interact with
their environments and how their environments shape their character.
Similarly, anthropologist Jane Goodall studied the behavior of
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22 Research Settings And Mehtods Of Data Collection relationships, hunting, and social behavior. Her early work served as the
basis for future research on chimpanzees and animal behavior in general.
Advantages of Observational Studies :
By observing events as they naturally occur, patterns in behav ior will
emerge and general questions will become more specific. The hypotheses
that result from these observations will guide the researcher in shaping
data into results.
One advantage of this type of research is the ability to make on -the-fly
adjustments to the initial purpose of a study. These observations also
capture behavior that is more natural than behavior occurring in the
artificial setting of a lab and that is relatively free of some of the bias seen
in survey responses. However, the researcher m ust be careful not to apply
his or her own biases to the interpretation. Researchers may also use this
type of data to verify external validity, allowing them to examine whether
study findings generalize to real world scenarios.
There are some areas of stu dy where observational studies are more
advantageous than others. This type of research allows for the study of
phenomena that may be unethical to control for in a lab, such as verbal
abuse between romantic partners. Observation is also particularly
advant ageous as a cross -cultural reference. By observing people from
different cultures in the same setting, it is possible to gain information on
cultural differences.
Disadvantages of Observational Studies :
While observational studies can generate rich qualita tive data, they do not
produce quantitative data, and thus mathematical analysis is limited.
Researchers also cannot infer causal statements about the situations they
observe, meaning that cause and effect cannot be determined. Behavior
seen in these studi es can only be described, not explained.
There are also ethical concerns related to observing individuals without
their consent. One way to avoid this problem is to debrief participants
after observing them and to ask for their consent at that time. Overt
observation, where the participants are aware of the researcher’s presence,
is another option to overcome this problem. However, this tactic does have
its drawbacks. When subjects know they are being watched, they may
alter their behavior in an attempt to make themselves look more
admirable.
This type of research can also be very time consuming. Some studies
require dozens of observation sessions lasting for several hours and
sometimes involving several researchers. Without the use of multiple
researchers, the chances of observer bias increase; because behavior is
perceived so subjectively, it is possible that two observers will notice
different things or draw different conclusions from the same behavior.
Observation without Intervention : munotes.in

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23 Research Methodology For Psychology • The goals of nat uralistic observation are to describe behavior as it
normally occurs and to examine relationships among variables.
• Naturalistic observation helps to establish the external validity of
laboratory findings.
• When ethical and moral considerations prevent experimental control,
naturalistic observation is an important research strategy.
Observation with Intervention :
• Most psychological research uses observation with intervention.
• The three methods of observation with intervention are participant
obser vation, structured observation, and the fi eld experiment.
• Whether “undisguised” or “disguised,” participant observation allows
researchers to observe behaviors and situations that are not usually
open to scientific observation.
• If individuals change their behavior when they know they are being
observed (“reactivity”), their behavior may no longer be
representative of their normal behavior.
• Often used by clinical and developmental psychologists, structured
observations are set up to record behavior s that may be diffi cult to
observe using naturalistic observation.
• In a field experiment, researchers manipulate one or more
independent variables in a natural setting to determine the effect on
behavior.
Structured Observation :
There are a variety of observational methods using intervention that are
not easily categorized. These procedures differ from naturalistic
observation because researchers intervene to exert some control over the
events they are observing. The degree of intervention and control o ver
events is less, however, than that seen in field experiments (which we
describe briefly in the next section and in more detail in Chapter 6). We
have labeled these procedures structured observation. Often the observer
intervenes in order to cause an ev ent to occur or to “set up” a situation so
that events can be more easily recorded.
2.2.2 Interviews :
Interviews are a type of qualitative data in which the researcher asks
questions to elicit facts or statements from the interviewee. Interviews
used for r esearch can take several forms:
Informal Interview : A more conversational type of interview, no
questions are asked and the interviewee is allowed to talk freely. General
interview guide approach: Ensures that the same general areas of
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24 Research Settings And Mehtods Of Data Collection the conversational approach, but still allows a degree of freedom and
adaptability in getting the information from the interviewee. Standardized,
open -ended interview: The same open -ended questions are asked to all
interviewees. This approach facilitates faster interviews that can be more
easily analyzed and compared.Closed, fixed -response interview
(Structured): All interviewees are asked the same questions and asked to
choose answers from among the sam e set of alternatives.
The interview may be regarded either as an alternative to other survey
methods or as a supplementary source of information. Although it is more
costly in both time and money than the questionnaire, it is also more
flexible. Additiona l information over and above initial plans can be readily
obtained and ambiguity and misunderstanding eliminated immediately.
One of the greatest strengths of the interview —direct verbal
communication —is also a source of weakness because variability is so
common in social interactions. For an interview to be successful, rapport
is generally required. It is most readily established when the interviewer is
nonjudgmental, supportive, and understanding. However, these very
characteristics lead to variability i n social interaction among those
interviewed. We could achieve sufficient control over social interactions
so that the interviews are more homogeneous. However, this would
inevitably lead to a sterile interview situation. This, in turn, would result in
less rapport, which, we have noted, is important for a good interview.
Other problems beset the interview, especially when there is more than
one interviewer. Different interviewers may vary in the way they ask
questions or interpret responses, or in the way respondents react to them.
Interviewer differences are common. How do we assess the comparability
of different interviewers? If you reflect a moment, you’ll realize that the
situation is similar to using several raters in noninterview settings and
determi ning the interrater reliability. In the present case, we are asking
whether there is inter -interviewer reliability.
One way to achieve greater inter -interviewer reliability is to standardize
the interview procedures. While this standardization increases t he
interview reliability, it decreases its flexibility. Because of these
weaknesses, the interview might best be reserved as an exploratory
method to generate ideas and hypotheses that can later be tested by the use
of other methods.
When personal intervie ws are used to collect survey data, respondents
are usually contacted in their homes or in a shopping mall and trained
interviewers administer the questionnaire. The personal interview allows
greater flexibility in asking questions than does the mail surve y. During an
interview the respondent can obtain clarifi cation when questions are
unclear, and the trained interviewer can follow up incomplete or
ambiguous answers to open ended questions. The interviewer controls the
order of questions and can ensure th at all respondents complete the
questions in the same order. Traditionally, the response rate to personal
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25 Research Methodology For Psychology Telephone Interviews :
The prohibitive cost of personal interviews and difficulties supervising
interviewers have led survey researchers to turn to telephone or Internet
surveys. Phone interviewing met with considerable criticism when it was
first used because of serious limitations on the sampling frame of potential
respondents. Many people had unli sted numbers, and the poor and those in
rural areas were less likely to have a phone. By 2000, however, more than
97% of all U.S. households had telephones (U.S. Census Bureau, 2000),
and households with unlisted numbers could be reached using random -
digit dialing. The random -digit dialing technique permits researchers to
contact efficiently a generally representative sample of U.S. telephone
owners. Telephone interviewing also provides better access to dangerous
neighborhoods, locked buildings, and respond ents available only during
evening hours (have you ever been asked to complete a telephone survey
during dinner?). Interviews can be completed more quickly when contacts
are made by phone, and interviewers can be better supervised when all
interviews are c onducted from one location. The telephone survey, like the
other survey methods, is not without its drawbacks. A possible selection
bias exists when respondents are limited to those who have telephones and
the problem of interviewer bias remains. There is a limit to how long
respondents are willing to stay on the phone, and individuals
2.3 QUESTIONNAIRE The questionnaire is more than simply a list of questions or forms to be
completed. When properly constructed, a questionnaire can be used as a
scientific instrument to obtain data from large numbers of individuals.
Construction of a useful questionnaire that minimizes interfering problems
requires experience, skill, thoughtfulness, and time. A major advantage of
the questionnaire is that data can be obtaine d on large numbers of
participants quickly and relatively inexpensively. Further, the sample can
be very large and geographically representative. Often, anonymity can be
easily maintained; that is, identifying information is not associated with
the data. W hen constructed properly, a questionnaire provides data that
can be organized easily, tabulated, and analyzed. Because of these
apparent advantages, the use of the questionnaire is a popular method.
There are two broad classes of questionnaires: descripti ve and analytical.
Descriptive questionnaires are usually restricted to factual information,
often biographical, which is usually accessible by other means. Job
application forms and U.S. Census questionnaires are typically of this
type. Analytical questio nnaires deal more with information related to
attitudes or opinions.
The results of a questionnaire are about as useful as the care and thought
that went into its preparation and dissemination. Just as in normal social
intercourse, the way questions are f ormulated and posed may present
problems. They may be ambiguous; they may suggest the answer that the
researcher “wants”; they may contain loaded words. Ambiguity is
relatively easy to eliminate. A pilot project, limited to a small number of munotes.in

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26 Research Settings And Mehtods Of Data Collection respondents, w ill usually uncover sources of ambiguity of which the
researcher was unaware. These may then be corrected. Table 6.7 illustrates
several examples of ambiguous and leading survey questions and also
suggests improved versions of the questions.
As much as we might wish it to be, completing questionnaires is not a
neutral task, devoid of feelings and emotions. Often respondents are
somewhat apprehensive about how they will appear in the researcher’s
eye. They want to look good and do well. Consequently, their r esponses
may reflect their interpretations of the investigator’s desires rather than
their own beliefs, feelings, or opinions. This is referred to as demand
characteristics. We will say more about this later. Obviously, questions
should be stated in a neut ral way and not in a way that suggests a
particular response. A fundamental requirement is that the question should
be answerable. If respondents are given answers from which to choose, the
options should be clear and independent. Also, different results c an occur
when open -ended or closed -ended questions are used. In some cases, the
questionnaire is sensitive to position effects. Respondents are more likely
to skip items placed toward the end of a questionnaire, and the answers are
also slightly different when answered.
More attention has been given to response bias than to other sources of
possible bias and contamination. As we noted earlier, results can be
markedly affected by the sample on which they are based. The problem of
sampling bias is compounded in mailed surveys because of the low return
rates. The actual sample on which the data analyses are based is generally
a subsample of the original sample. Low returns make it difficult to assess
the representativeness of the final sample. It is safe to as sume that it is
biased and that those who participated in the survey are different in some
way from those who did not. How important is this difference? It may be
considerable, or it may be trivial. Because its importance cannot be
assessed, any generaliza tions based on low returns must be restricted. For
this reason, it is important to know the return rate on survey research.
Unfortunately, some studies fail to provide this information. Other things
being equal, the higher the return rate, the better the s urvey.
A number of factors affect return/response rates. Some are quite costly, so
that economic factors must be balanced against the greater generality
permitted by higher rates of return. Methods to increase return rate include
follow -up contacts, gener al delivery and pickup, use of closed -ended
rather than open -ended questions wherever possible, use of rewards for
participation, and limiting the length of time needed to complete the
survey.
Instruments and Inventories are questionnaires that have stood the test
of time. That is, they were designed to measure particular attributes and
have been demonstrated to do so with validity and reliability. Examples
include personality tests, aptitude tests, and achievement tests. Personality
tests measure some sta te or trait of an individual. Examples include the
Minnesota Multiphasic Personality Inventory (MMPI), Beck Depression
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27 Research Methodology For Psychology Sixteen Personality Factors Questionnaire (16PF). Aptitude tests measu re
some skill or ability. Examples include the Stanford –Binet Intelligence
Scale, the Wechsler Adult Intelligence Scale (WAIS -III), the Wechsler
Intelligence Scale for Children (WISC -III), and the Graduate Record
Examination (GRE). Achievement tests measur e competence in a
particular area. Examples include the Stanford Achievement tests that
students take as they progress through K –12 grades in school; state
licensing exams for teachers, counselors, lawyers, physicians and other
professionals; and the major field achievement test that psychology majors
at some universities take just prior to graduation.
If you consider a research project in which a questionnaire might be used,
it would be wise to determine whether an instrument or inventory already
exists t o measure the variable of interest. Don’t reinvent the wheel. If
someone else has already invested the time and effort to develop a
measure with known validity and reliability, use it. One of the
characteristics of science is that we make information publi c and continue
to build upon what others have done.
2.4 SURVEY RESEARCH Most surveys involve asking a standard set of questions to a group of
participants. In a highly structured survey, subjects are forced to choose
from a response set such as “strongly disagree, disagree, undecided, agree,
strongly agree”; or “0, 1 -5, 6-10, etc.” One of the benefits of having
forced -choice items is that each response is coded so that the results can
be quickly entered and analyzed using statistical software. While this type
of survey typically yields surface information on a wide variety of factors,
they may not allow for an in -depth understanding of human behavior.
Of course, surveys can be designed in a number of ways. Some surveys
ask open -ended questions, allowing ea ch participant to devise their own
response, allowing for a variety of answers. This variety may provide
deeper insight into the subject than forced -choice questions, but makes
comparing answers challenging. Imagine a survey question that asked
participant s to report how they are feeling today. If there were 100
participants, there could be 100 different answers, which is more
challenging and takes more time to code and analyze.
Surveys are useful in examining stated values, attitudes, opinions, and
reporti ng on practices. However, they are based on self-report, and this
can limit accuracy. For a variety of reasons, people may not provide
honest or complete answers. Participants may be concerned with
projecting a particular image through their responses, the y may be
uncomfortable answering the questions, inaccurately assess their behavior,
or they may lack awareness of the behavior being assessed. So, while
surveys can provide a lot of information for many participants quickly and
easily, the self -reporting m ay not be as accurate as other methods.
The survey method of data collection is a type of descriptive research, and
is likely the most common of the major methods. Surveys have limited use munotes.in

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28 Research Settings And Mehtods Of Data Collection for studying actual social behavior but are an excellent way to gai n an
understanding of an individual’s attitude toward a matter.
Similar to an interview, a survey may use close -ended questions, open -
ended questions, or a combination of the two. “Closed -ended questions”
are questions that limit the person taking the surv ey to choose from a set
of responses. Multiple choice, check all that apply, and ratings scale
questions are all examples of closed -ended questions. “Open -ended
questions” are simply questions that allow people to write in their own
response.
Surveys are a highly versatile tool in psychology. Although a researcher
may choose to only administer a survey to sample of individuals as their
entire study, surveys are often used in experimental research as well. For
example, a researcher may assign one group of in dividuals to an
experimental condition in which they are asked to focus on all the negative
aspects of their week to induce a negative mood, while he assigns another
group of people to a control group in which they read a book chapter.
After the mood induc tion, he has both groups fill out a survey about their
current emotions. In this example, the mood induction condition is the
independent (manipulated) variable, while participants’ responses on the
emotion survey is the dependent (measured) variable.
Cros s-Sectional Design :
• In the cross -sectional design, one or more samples are drawn from the
population(s) at one time.
• Cross -sectional designs allow researchers to describe the
characteristics of a population or the differences between two or more
popu lations, and correlational findings from cross -sectional designs
allow researchers to make predictions.
Longitudinal Design
• In the longitudinal design, the same respondents are surveyed over
time in order to examine changes in individual respondents.
• Because of the correlational nature of survey data, it is difficult to
identify the causes of individuals’ changes over time.
• As people drop out of the study over time (attrition), the final sample
may no longer be comparable to the original sample or represent the
population.
There are primarily three modes of data collection that can be employed to
gather feedback – Mail, Phone, and Online. The method actually used for
data-collection is really a cost -benefit analysis:
Mail Surveys :
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29 Research Methodology For Psychology Cons: Expensive, data collection errors lag time
Phone Surveys :
Pros: High degree of confidence in the data collected, reach almost anyone
Cons: Expensive, cannot self -administer, need to hire an agency
Web/Online Surveys :
Pros: Cheap, can self -administer, very low probability of data errors
Cons: Not all your customers might have an email address/be on the
internet, customers may be wary of divulging information online.
Multi -Mode Surveys :
Surveys, where the data is collecte d via different modes (online, paper,
phone etc.), is also another way of going. It is fairly straightforward and
easy to have an online survey and have data -entry operators to enter in
data (from the phone as well as paper surveys) into the system. The sa me
system can also be used to collect data directly from the respondents.
Writing Great Questions for data collection :
Writing great questions can be considered by an art. Art always requires a
significant amount of hard work, practice, and help from other s.
Avoid loaded or leading words or questions :
A small change in content can produce effective results. Words such as
could, should, might are all used for almost the same purpose, but may
produce a 20% difference in agreement to a question. For example, “ The
management could.. should.. might.. have shut the factory”.
Intense words such as – prohibit or action, which represent control or
action also produce similar results. For example, “Do you believe that
Donald Trump should prohibit insurance companies from raising rates?”.
Sometimes the content is just biased. For instance, “You wouldn’t want to
go to Rudolpho’s Restaurant for the organization’s annual party, would
you?”
Misplaced questions :
Questions should always have reference to the intended context , questions
placed out of order or without its requirement should be avoided.
Generally, a funnel approach should be implemented – generic questions
should be included in the initial section of the questionnaire as a warm -up
and specific ones should follow and towards the end, demographic or
geographic questions should be included.
Mutually non -overlapping response categories : munotes.in

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30 Research Settings And Mehtods Of Data Collection Multiple choice answers should be mutually unique in order to provide
distinct choices. Overlapping answer options frustrate the res pondent and
make interpretation difficult at best. Also, the questions should always be
precise.
For example: “Do you like water juice?”
This question is vague. In which terms is the liking for orange juice is to
be rated? – Sweetness, texture, price, nutr ition etc.
Avoid the use of confusing/unfamiliar words :
Asking about industry related terms such as caloric content, bits, bytes,
mbs, and other such terms and acronyms can be confusing for
respondents. Ensure that the audience understands your language le vel,
terminology and above all, the question you ask.
Non-directed questions give respondents excessive leeway :
What suggestions do you have for improving our shoes? The question is
about quality in general, but the respondent may offer suggestions about
texture, the type of shoes or variants.
Never force questions :
There will always be certain questions which cross certain privacy rules
and since privacy is an important issue for most people, these questions
should either be eliminated from the survey or n ot kept as mandatory.
Survey questions about income, family income and status, religious, and
political beliefs etc. should always be avoided as they are considered to be
intruding and respondents can choose not to answer them.
Unbalanced answer options in scales :
Unbalanced answer options in scales such as Likert Scale and Semantic
Scale may be appropriate for some situations and biased in others. When
analyzing a pattern in eating habits, a study used a quantity scale that
made obese people appear in the middle of the scale with the polar ends
reflecting a state where people starve and an irrational amount to consume.
There are cases where we usually would not expect poor service such as
hospitals.
Questions which cover two points :
What is the fastest and most convenient ISP for your location? The fastest
ISP would be expensive and the less expensive ones will most likely be
slow. To understand both factors, two separate questions should be asked.
Dichotomous questions :
Dichotomous questions are used in cas e you want a distinct answer, for
example – Yes/No, Male/Female. For example, the question “Do you
think Hillary Clinton will win the election?” – The answer can either be
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31 Research Methodology For Psychology Avoid the use of long questions :
The use of long questions will definitel y increase the time taken for
completion which will generally lead to an increase in the survey dropout
rate. Multiple choice questions are the longest and most complex and
open -ended questions are the shortest and easiest to answer.
Advantages of Surveys :
The benefits of this method include its low cost and its large sample size.
Surveys are an efficient way of collecting information from a large sample
and are easy to administer compared with an experiment. Surveys are also
an excellent way to measure a w ide variety of unobservable data, such as
stated preferences, traits, beliefs, behaviors, and factual information. It is
also relatively simple to use statistical techniques to determine validity,
reliability, and statistical significance.
Surveys are flex ible in the sense that a wide range of information can be
collected. Since surveys are a standardized measure, they are relatively
free from several types of errors. Only questions of interest to the
researcher are asked, codified, and analyzed. Survey res earch is also a
very affordable option for gathering a large amount of data.
Disadvantages of Surveys :
The major issue with this method is its accuracy: since surveys depend on
subjects’ motivation, honesty, memory, and ability to respond, they are
very su sceptible to bias. There can be discrepancies between respondents’
stated opinions and their actual opinions that lead to fundamental
inaccuracies in the data. If a participant expects that one answer is more
socially acceptable than another, he may be mor e motivated to report the
more acceptable answer than an honest one.
When designing a survey, a researcher must be wary of the wording,
format, and sequencing of the questions, all of which can influence how a
participant will respond. In particular, a res earcher should be concerned
with the reliability of their survey. “Reliability” concerns the degree to
which the survey questions are likely to yield consistent results each time.
A survey is said to have high reliability if it produces similar results eac h
time. For example, a reliable measure of emotion is one that measures
emotion the same way each time it is used. However, for a survey to be
useful, it needs to be not only reliable, but valid. If a measure is has high
“validity”, this means that it is i n fact measuring the concept it was
designed to measure (in this case, emotion). It is important to note that a
survey can be reliable, but not valid (and vice versa). For example, just
because our emotion survey is reliable, and provides us with consisten t
results each time we administer it, does not necessarily mean it is
measuring the aspects of emotion we want it to. In this case, our emotion
survey is reliable, but not necessarily valid.
Structured surveys, particularly those with closed -ended question s, may
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32 Research Settings And Mehtods Of Data Collection tend to be self -selected since the the respondents must choose to complete
the survey. Surveys are not appropriate for studying complex social
phenomena since they do not give a full sense of these processes.
Information not gathered as part of a controlled experiment or from
random assignment of study subjects. Nonexperimental data are
commonly used in social science research, particularly when gathering
experimental data would b e too costly or unethical. Because the researcher
cannot control assignment of subjects to the treatment and control groups,
nonexperimental data are more difficult than experimental data to analyze
and interpret. Examples of nonexperimental data include s urvey data,
administrative records, and standardized test scores. They also are known
as observational data.
Case study provides a systematic and scientific way of perceiving or
examining the events, collecting data, analysing information, and
preparing a report. As a result the researcher may gain a sharpened
understanding of why the instance happened as it did, and what might
become important to look at more extensively in future research. Case
studies lend themselves to both generating and testing hypot heses. In other
words, case study should be defined as a research strategy, an inquiry that
investigates a phenomenon within its real -life context. Case study research
means single and multiple case studies, can include quantitative evidence,
relies on mul tiple sources of evidence and benefits from the prior
development of theoretical propositions. Case studies are based on
evidence of quantitative and qualitative research. Single subject -research
provides the statistical framework for making inferences fro m quantitative
case-study data. According to Lamnek (2005) “The case study is a
research approach, situated between concrete data taking techniques and
methodologic paradigms.” In the past years, case study method was used
in the field of clinical psycholo gy to examine the patient’s previous
history regarding the person’s mental health status. To know about the
patient’s physical and mental health, and to make an accurate diagnosis, it
is very important to know about the patient’s past and present health
related as well as environmental related problems and issues.
Aside from consulting the primary origin or source, data can also be
collected through a third party, a process common with secondary data. It
takes advantage of the data collected from previous r esearch and uses it to
carry out new research.
Secondary data is one of the two main types of data, where the second
type is the primary data. These 2 data types are very useful in research and
statistics, but for the sake of this article, we will be restr icting our scope to
secondary data. Sources of secondary data include books, personal
sources, journals, newspapers, websitess, government records etc.
Secondary data are known to be readily available compared to that of
primary data. It requires very litt le research and needs for manpower to
use these sources.
Archival Records : munotes.in

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33 Research Methodology For Psychology • Archival records are the public and private documents describing the
activities of individuals, groups, institutions, and governments, and
comprise running records and records of specifi c, episodic events.
• Archival data are used to test hypotheses as part of the multimethod
approach, to establish the external validity of laboratory findings, and
to assess the effects of natural treatments.
• Potential problems associated with archival records include selective
deposit, selective survival, and the possibility of spurious
relationships. Consider for a moment all of the data about you that
exist in various records: birth certificate; school enrollment and
grades; credit/debit car d purchases; driver’s license, employment and
tax records; medical records; voting history;e -mail, texting, and cell
phone accounts; and if you’re active on sites such as
Facebook and Twitter, countless entries describing your daily life. Now
multiply this by the millions of other people for whom similar records
exist and you will only touch upon the amount of data “out there.” Add to
this all of the data available for countries, governments, institutions,
businesses, media, and you will begin to appreciate the wealth of data
available to psychologists to describe people’s behavior using archival
records. Archival records are the public and private documents describing
the activities of individuals, groups, institutions, and governments.
Records that are con tinuously kept and updated are referred to as running
records. The records of your academic life (e.g., grades, activities) are an
example of running records, as are the continuous records of sports teams
and the stock market. Other records, such as person al documents (e.g.,
birth certificates, marriage licenses), are more likely to describe specific
events or episodes, and are referred to as episodic records
2.6 REFERENCES 1. Shaughnessy, J. J., Zechmeister, E. B. &Zechmeister, J. (2012).
Research method s in psychology . (9th ed..). NY: McGraw Hill.
2. Elmes, D. G. (2011). Research Methods in Psychology (9thed.).
Wadsworth Publishing.
3. Goodwin, J. (2009). Research in Psychology: Methods in Design
(6thed.). Wiley.
4. McBurney, D. H. (2009). Research methods . (8th Ed.). Wadsworth
Publishing.
5. Forrester, M. A. (2010). Doing Qualitative Research in Psychology: A
Practical Guide . Sage.


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34
3
EXPERIMENTAL AND QUASI -
EXPERIMENTAL METHODS
Unit Structure
3.1 Introduction
3.1.1 Why Psychologists Conduct Experiments
3.1.2 Experimental Methods
3.2 Independent groups designs
3.3 Repeated measures designs
3.3.1The role of practice effects in repe ated measures designs
3.3.2 Balancing Practice Effects in the Complete Design
3.3.3 Block Randomization
3.3.4 ABBA Counterbalancing:
3.3.5 Balancing Practice Effects in the Incomplete Design
3.3.6 The problem of differential transfer
3.4 Complex designs
3.4.1Describing effects in a complex design
3.4.2 Main Effects and Interaction Effects
3.4.3 Complex Designs with Three Independent Variables
3.4.4 Interaction Effects and Ceiling and Floor Effects
3.5 Quasi -experimental designs and program evaluation
3.5.1 The Nonequivalent Control Group Design
3.5.2 Interrupted Time -Series Designs
3.5.3 Program evaluation
3.6 References
3.1 INTRODUCTION We introduced you to the four goals of research in psychology:
description, prediction, explanation, and application . Psychologists use
observational methods to develop detailed descriptions of behavior, often
in natural settings. Survey research methods allow psychologists to
describe people’s attitudes and opinions. Psychologists are able to make
predictions about beh avior and mental processes when they discover
measures and observations that co -vary (correlations). Description and
prediction are essential to the scientific study of behavior, but they are not
sufficient for understanding the causes of behavior. Psychol ogists also
seek explanation —the “why” of behavior. We achieve scientific
explanation when we identify the causes of a phenomenon. munotes.in

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35 Research Methodology For Psychology We will explore how the experimental method is used to test
psychological theories as well as to answer questions of practica l
importance. As we have emphasized, the best overall approach to research
is the multimethod approach. We can be more confident in our
conclusions when we obtain comparable answers to a research question
after using different methods. Our conclusions are then said to have
convergent validity. Each method has different shortcomings, but the
methods have complementary strengths that overcome these shortcomings.
The special strength of the experimental method is that it is especially
effective for establishin g cause -and-effect relationships. In this chapter we
discuss the reasons researchers conduct experiments and we examine the
underlying logic of experimental research. Our focus is on a commonly
used experimental design —the random groups design. We describe the
procedures for forming random groups and the threats to interpretation
that apply specifically to the random groups design. Then we describe the
procedures researchers use to analyze and interpret the results they obtain
in experiments, and also explo re how researchers establish the external
validity of experimental findings. We conclude the chapter with
consideration of two additional designs involving independent groups: the
matched groups design and the natural groups design.
3.1.1 Why Psychologists Conduct Experiments :
• Researchers conduct experiments to test hypotheses about the causes
of behavior.
• Experiments allow researchers to decide whether a treatment or
program effectively changes behavior.
One of the primary reasons that psychologists conduct experiments is to
make empirical tests of hypotheses they derive from psychological
theories. For example, Pennebaker (1989) developed a theory that keeping
in thoughts and feelings about painful experiences might take a physical
toll. According to this “inhibition theory,” it’s physically stressful to keep
these experiences to oneself.
3.1.2 Experimental Methods :
Pennebaker and his colleagues conducted many experiments in which they
assigned one group of participants to write about personal emotion al
events and another group to write about superficial topics. Consistent with
the hypotheses derived from the inhibition theory, participants who wrote
about emotional topics had better health outcomes than participants who
wrote about superficial topics. Not all the results, however, were
consistent with the inhibition theory. For example, students asked to dance
expressively about an emotional experience did not experience the same
health benefits as students who danced and wrote about their experience.
Pennebaker and Francis (1996) did a further test of the theory and
demonstrated that cognitive changes that occur through writing about
emotional experiences were critical in accounting for the positive health
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36 Experimental And Quasi-Experimental Methods Our brief description of testing the inhibition theory illustrates the general
process involved when psychologists do experiments to test a hypothesis
derived from a theory. If the results of the experiment are consistent with
what is predicted by the hypothesis, then the theory receives sup port. On
the other hand, if the results differ from what was expected, then the
theory may need to be modified and a new hypothesis developed and
tested in another experiment.
Testing hypotheses and revising theories based on the outcomes of
experiments ca n sometimes be a long and painstaking process, much like
combining the pieces to a puzzle to form a complete picture. The self -
correcting interplay between experiments and proposed explanations is a
fundamental tool psychologists use to understand the caus es of the ways
we think, feel, and behave.
Well -conducted experiments also help to solve society’s problems by
providing vital information about the effectiveness of treatments in a wide
variety of areas. This role of experiments has a long history in the fi eld of
medicine (Thomas, 1992). For example, near the beginning of the 19th
century, typhoid fever and delirium tremens were often fatal. The standard
medical practice at that time was to treat these two conditions by bleeding,
purging, and other simila r “therapies.” In an experiment to test the
effectiveness of these treatments, researchers randomly assigned one
group to receive the standard treatment (bleeding, purging, etc.) and a
second group to receive nothing but bed rest, good nutrition, and close
observation. Thomas (1992) describes the results of this experiment as
“unequivocal and appalling” (p. 9): The group given the standard medical
treatment of the time did worse than the group left untreated. Treating
such conditions using early -19th-centur y practices was worse than not
treating them at all! Experiments such as these contributed to the insight
that many medical conditions are self -limited: The illness runs its course,
and patients recover on their own.
3.2 INDEPENDENT GROUPS DESIGNS Indepen dent measures design, also known as between -groups, is an
experimental design where different participants are used in each
condition of the independent variable. This means that each condition of
the experiment includes a different group of participants.
This should be done by random allocation, which ensures that each
participant has an equal chance of being assigned to one group or the
other.
Independent measures involve using two separate groups of participants;
one in each condition.
The independent groups design is an experimental design whereby two
groups are exposed to different experimental conditions. Usually half of
the participants are assigned to the experimental group where they are
exposed to a condition where the independent variable is ma nipulated. The munotes.in

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37 Research Methodology For Psychology other half are assigned to a control group for comparison, where no such
manipulation occurs.One advantage of using this design is that there are
no order effects which affect the outcomes of the experiment. These
happen when participants ta ke part in both conditions of the experiment,
and their performance differs across conditions as a result. For example,
the practice at doing a memory task felt after the first condition could lead
to better performance on the second memory task, irrespect ive of the
manipulation of the independent variable.One disadvantage of this design
is differences between the experimental and control groups may be due to
individual differences between participants., rather than the effect of the
independent variable. F or example, due to chance, one group may have a
better working memory than the other, and when given a memory task,
that group will perform better, regardless of the independent variable
manipulation, due to pre -disposed advantage. This could be mitigated with
random sampling of participants.
Example: a series of five experiment using independent -groups designs,
the researchers examined the hypothesized effects of the color red on
men’s perceptions of women. In each study, the participants were shown a
photograph of a woman. While the woman depicted remained the same,
the background color was varied across different conditions. Thus,
independent groups comparisons were made for red background vs.
backgrounds that were white, gray, blue, or green. After a bri ef view (5
seconds) of the picture, each participant assessed the woman shown for
(general) attractiveness, intelligence, likeability, kindness, and several
measures of sexual desirability. In one of the five experiments, a small
sample of women also asses sed the attractiveness of the women shown.
The participants included 172 men and 32 women, all college
undergraduates.
The researchers found statistically significant effects of the color red on
men’s perceptions of sexual attractiveness of women. Interest ingly, the
color red had no effect on women evaluating other women or on men’s
evaluation of women’s nonsexual attributes, such as intelligence,
likeability, or kindness. The results provide strong support for the
hypothesized “red effect.” Even a brief (5 -second) glimpse of red
enhances men’s attraction to women. Similar results have been reported
for other animals. The researchers discuss their results and implications
for studies in interpersonal and sexual attraction.
The logic of the experimental metho d and the application of control
techniques that produce internal validity can be illustrated in an
experiment investigating girls’ dissatisfaction with their body, conducted
in the United Kingdom by Dittmar, Halliwell, and Ive (2006). Their goal
was to de termine whether exposure to very thin body images causes young
girls to experience negative feelings about their own body. Many
experiments conducted with adolescent and adult participants demonstrate
that women report greater dissatisfaction about themsel ves after exposure
to a thin female model compared to other types of images. Dittmar and her
colleagues sought to determine whether similar effects are observed for
girls as young as 5 years old. The very thin body image they tested was the munotes.in

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38 Experimental And Quasi-Experimental Methods Barbie doll. An thropological studies that compare the body proportions of
Barbie to actual women reveal that the Barbie doll has very unrealistic
body proportions, yet Barbie has become a sociocultural ideal for female
beauty (see Figure 6.1).
In the experiment small gro ups of young girls (51D 2 –61D 2 years old)
were read a story about “Mira” as she went shopping for clothes and
prepared to go to a birthday party. As they heard the story, the girls looked
at picture books with six scenes related to the story. In one condi tion of
the experiment, the picture books had images of Barbie in the scenes of
the story (e.g., shopping for a party outfit, getting ready for the party). In a
second condition the picture books had similar scenes but the fi gure
pictured was the “Emme” d oll. The Emme fashion doll is an attractive doll
with more realistic body proportions, representing a U.S. dress size 16 (see
Figure 6.2). Finally, in the third condition of the experiment the picture
books did not depict Barbie or Emme (or any body) but, instead, showed
neutral images related to the story (e.g., windows of clothes shops,
colorful balloons). These three versions of the picture books (Barbie,
Emme, neutral) represent three levels of the independent variable that was
manipulated in the experi ment. Because different groups of girls
participated in each level of the independent variable, the experiment is
described as an independent groups design.
Manipulation :
Dittmar et al. (2006) used the control technique of manipulation to test
their hypot heses about girls’ body dissatisfaction. The three conditions of
the independent variable allowed these researchers to make comparisons
relevant to their hypotheses. If they tested only the Barbie condition, it
would be impossible to determine whether thos e images influenced girls’
body dissatisfaction in any way. Thus, the neutral -image condition created
a comparison —a way to see if the girls’ body dissatisfaction differed
depending on whether they looked at a thin ideal vs. neutral images. The
Emme condit ion added an important comparison. It is possible that any
images of bodies might influence girls’ perceptions of themselves. Dittmar
and her colleagues tested the hypothesis that only thin body ideals, as
represented by Barbie, would cause body dissatisfa ction.
At the end of the story, the young girls turned in their picture books and
completed a questionnaire designed for their age level. Although Dittmar
and her colleagues used a number of measures designed to assess the girls’
satisfaction with their bo dy, we will focus on one measure, the Child
Figure Rating Scale. This scale has two rows of seven line drawings of
girls’ body shapes ranging from very thin to very overweight. Each girl
was asked first to color in the figure in the top row that most looks like her
own body right now (a measure of perceived actual body shape). Then, on
a second row of the same figures, the girls were asked to color in the
figure that shows the way they most want to look (ideal body shape). Girls
were told they could pick an y of the figures and that they could choose the
same figure in each row. A body shape dissatisfaction score, the
dependent variable, was computed by counting the number of figures munotes.in

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39 Research Methodology For Psychology between each girl’s actual shape and her ideal shape. A score of zero
indica ted no body shape dissatisfaction, a negative score indicated a desire
to be thinner, and a positive score indicated a desire to be bigger.
Holding Conditions Constant :
In Dittmar et al.’s experiment, several factors that could have affected the
girls’ at titudes toward their body were kept the same across the three
conditions. All of the girls heard the same story about shopping and
attending a birthday party, and they looked at their picture books for the
same amount of time. They all received the same in structions throughout
the experiment and received the exact same questionnaire at the
conclusion.
Researchers use holding conditions constant to make sure that the
independent variable is the only factor that differs systematically across
the groups. If th e three groups had differed on a factor other than the
picture books, then the results of the experiment would have been
uninterpretable. Suppose the participants in the Barbie condition had heard
a different story, for example, a story about Barbie being thin and popular.
We wouldn’t know whether the observed difference in the girls’ body
dissatisfaction was due to viewing the images of Barbie or to the different
story. When the independent variable of interest and a different, potential
independent variab le are allowed to c -ovary, a confounding is present.
When there are no confoundings, an experiment has internal validity.
Holding conditions constant is a control technique that researchers use to
avoid confoundings. By holding constant the story the girls heard in the
three conditions, Dittmar and her colleagues avoided confoundings by this
factor. In general, a factor that is held constant cannot possibly co -vary
with the manipulated independent variable. More importantly, a factor that
is held constant d oes not change, so it cannot possibly covary with the
dependent variable either. Thus, researchers can rule out factors that are
held constant as potential causes for the observed results.
It is important to recognize, however, that we choose to control on ly those
factors we think might influence the behavior we are studying —what we
consider plausible alternative causes. For instance, Dittmar et al. held
constant the story the girls heard in each condition. It is unlikely, however,
that they controlled fact ors such as the room temperature to be constant
across the conditions because room temperature probably would not likely
affect body image (at least when varying only a few degrees).
Nevertheless, we should constantly remain alert to the possibility that
there may be confounding factors in our experiments whose influence we
had not anticipated or considered.
Balancing :
Clearly, one key to the logic of the experimental method is forming
comparable (similar) groups at the start of the experiment. The
partici pants in each group should be comparable in terms of various
characteristics such as their personality, intelligence, and so forth (also munotes.in

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40 Experimental And Quasi-Experimental Methods known as individual differences). The control technique of balancing is
required because these factors often cannot be held constant. The goal of
random assignment is to establish equivalent groups of participants by
balancing, or averaging, individual differences across where R1, R2, and
R3 refer to the random assignment of subjects to the three independent
conditions of the experiment; X1 is one level of an independent variable
(e.g., Barbie), X2 is a second level of the independent variable (e.g.,
Emme), and X3 is a third level of the independent variable (e.g., neutral
images). An observation of behavior (O1) in each gr oup is then made.
Block Randomization :
Block randomization balances subject characteristics and potential
confoundings that occur during the time in which the experiment is
conducted, and it creates groups of equal size. Common procedure for
carrying out r andom assignment is block randomization. First, let us
describe exactly how block randomization is carried out, and then we will
look at what it accomplishes. Suppose we have an experiment with five
conditions (labeled, for convenience, as A, B, C, D, and E). One “block” is
made up of a random order of all five conditions:
One block of conditions ’! Random order of conditions A B C D E C A E
B D
In block randomization, we assign subjects to conditions one block at a
time. In our example with five condition s, five subjects would be needed
to complete the first block with one subject in each condition. The next
five subjects would be assigned to one of each of the five conditions to
complete a second block, and so on. If we want to have 10 subjects in each
of five conditions, then there would be 10 blocks in the block -randomized
schedule. Each block would consist of a random arrangement of the fi ve
conditions. This procedure is illustrated below for the first 11 participants.
Threats to Internal Validity :
• Randomly assigning intact groups to different conditions of the
independent variable creates a potential confounding due to
preexisting differences among participants in the intact groups.
• Block randomization increases internal validity by balancing
extraneous variables across conditions of the independent variable.
• Selective subject loss, but not mechanical subject loss, threatens the
internal validity of an experiment.
• Placebo control groups are used to control for the problem of demand
character istics, and double -blind experiments control both demand
characteristics and experimenter effects.
3.3 REPEATED MEASURES DESIGNS munotes.in

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41 Research Methodology For Psychology Repeated Measures design is an experimental design where the same
participants take part in each condition of the independent variable. This
means that each condition of the experiment includes the same group of
participants. Repeated Measures design is also known as within groups, or
within -subjects design.
Thus far we have considered experiments in which subjects participate in
only one condition of the experiment. They are randomly assigned to one
condition in the random groups and matched groups designs, or they are
selected to be in one group in natural groups designs. These independent
groups designs are powerful tools for studying the effects of a wide range
of independent variables.
There are times, however, when it is more effective to have each subject
participate in all the conditions of an experiment. These designs are called
repeated measures designs (or within -subje cts designs). In an independent
groups design, a separate group serves as a control for the group given the
experimental treatment. In a repeated measures design, subjects serve as
their own controls because they participate in both the experimental and
control conditions.
We begin this chapter by exploring the reasons why researchers choose to
use a repeated measures design. We then describe one of the central
features of repeated measures designs. Specifically, in repeated measures
designs, participants c an undergo changes during the experiment as they
are repeatedly tested. Participants may improve with practice, for example,
because they learn more about the task or because they become more
relaxed in the experimental situation. They also may get worse w ith
practice —for example, because of fatigue or reduced motivation. These
temporary changes are called practice effects. We described in Chapter 6
that individual differences among participants cannot be eliminated in the
random groups design, but they can be balanced by using random
assignment. Similarly, the practice effects that participants experience due
to repeated testing in the repeated measures designs cannot be eliminated.
Like individual differences in the random groups design, however,
practice effects can be balanced, or averaged, across the conditions of a
repeated measures design experiment.
When balanced across the conditions, practice effects are not confounded
with the independent variable and the results of the experiment are
interpretabl e. Our primary focus in this chapter is to describe the
techniques that researchers can use to balance practice effects. We also
introduce data analysis procedures for repeated measures designs. We
conclude the chapter with a consideration of problems that can arise in
repeated measures designs.
Researchers choose to use a repeated measures design in order to
(1) Conduct an experiment when few participants are available,
(2) Conduct the experiment more efficiently, munotes.in

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42 Experimental And Quasi-Experimental Methods (3) Increase the sensitivity of the exp eriment, and (4) study changes in
participants’ behavior over time.
Researchers gain several advantages when they choose to use a repeated
measures design. First, repeated measures designs require fewer
participants than an independent groups design, so th ese designs are ideal
for situations in which only a small number of participants is available.
Researchers who do experiments with children, the elderly or special
populations such as individuals with brain injuries frequently have a small
number of parti cipants available.
3.3.1The role of practice effects in repeated measures designs :
• Repeated measures designs cannot be confounded by individual
differences variables because the same individuals participate in each
condition (level)of the independent va riable.
• Participants’ performance in repeated measures designs may change
across conditions simply because of repeated testing (not because of
the independent variable); these changes are called practice effects.
• Practice effects may threaten the int ernal validity of a repeated
measures experiment when the different conditions of the independent
variable are presented in the same order to all participants.
• There are two types of repeated measures designs (complete and
incomplete) that differ in the specific ways in which they control for
practice effects.
The repeated measures designs have another important advantage in
addition to the ones we have already described. In a repeated measures
design, the characteristics of the participants cannot confo und the
independent variable being manipulated in the experiment. The same
participants are tested in all the conditions of a repeated measures design,
so it is impossible to end up with brighter, healthier, or more motivated
participants in one condition than in another condition. Stated more
formally, there can be no confounding by individual differences variables
in repeated measures designs. The absence of the potential for
confounding by individual differences variables is a great advantage of the
repeated measures designs. This does not mean, however, that there are no
threats to the internal validity of experiments that are done using repeated
measures designs.
3.3.2 Balancing Practice Effects in the Complete Design :
• Practice effects are balanced i n complete designs within each
participant using block randomization or ABBA counterbalancing.
• In block randomization, all of the conditions of the experiment (a
block) are randomly ordered each time they are presented.
• In ABBA counterbalancing, a ra ndom sequence of all conditions is
presented, followed by the opposite of the sequence. munotes.in

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43 Research Methodology For Psychology • Block randomization is preferred over ABBA counterbalancing when
practice effects are not linear, or when participants’ performance can
be affected by anticipation e ffects.
3.3.3 Block Randomization :
We introduced block randomization in Chapter 6 as an effective technique
for assigning participants to conditions in the random groups design.
Block randomization can also be used to order the conditions for each
partici pant in a complete design. For instance, Sackeim et al. administered
each of the three versions of their photographs (left composite, original,
and right composite) 18 times to each participant. The sequence of 54
trials is broken up into 18 blocks of 3 tr ials. Each block of trials contains
the three conditions of the experiment in random order. In general, the
number of blocks in a block randomized schedule is equal to the number
of times each condition is administered, and the size of each block is equal
to the number of conditions in the experiment.
3.3.4 ABBA Counterbalancing :
In its simplest form, ABBA counterbalancing can be used to balance
practice effects in the complete design with as few as two administrations
of each condition. ABBA counterbalanc ing involves presenting the
conditions in one sequence (i.e., A then B) followed by the opposite of
that same sequence (i.e., B then A). Its name describes the sequences
when there are only two conditions (A and B) in the experiment, but
ABBA counterbalanc ing is not limited to experiments with just two
conditions. Sackeim et al. could have presented the versions of their
photographs according to the ABBA sequence outlined in the top row of
Ta ble 7.2 labeled “Condition.” Note that in this case it literally would be
ABCCBA since there are three conditions.
3.3.5 Balancing Practice Effects in the Incomplete Design
• Practice effects are balanced across subjects in the incomplete design
rather than for each subject, as in the complete design.
• The rule for b alancing practice effects in the incomplete design is that
each condition of the experiment must be presented in each ordinal
position (first, second, etc.) equally often.
• The best method for balancing practice effects in the incomplete
design with four or fewer conditions is to use all possible orders of the
conditions.
• Two methods for selecting specific orders to use in an incomplete
design are the Latin Square and random starting order with rotation.
• Whether using all possible orders or selected orders, participants
should be randomly assigned to the different sequences.
The preferred technique for balancing practice effects in the incomplete
design is to use all possible orders of the conditions. Each participant is munotes.in

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44 Experimental And Quasi-Experimental Methods randomly assigned to one of t he orders. With only two conditions there are
only two possible orders (AB and BA); with three conditions there are six
possible orders (ABC, ACB, BAC, BCA, CAB, CBA). In general, there
are N! (Which is read “N factorial”) possible orders with N conditions ,
where N! Equals N(N - 1) (N - 2) . . . (N - [N - 1]). As we just saw, there
are six possible orders with three conditions, which is 3! (3 x 2 x 1 - 6).
The number of required orders increases dramatically with increasing
numbers of conditions. For insta nce, for five conditions there are 120
possible orders, and for six conditions there are 720 possible orders.
Because of this, the use of all possible orders is usually limited to
experiments involving four or fewer conditions.
We have just described the p referred method for balancing practice effects
in the incomplete design, all possible orders. There are times, however,
when the use of all possible orders is not practical. For example, if we
wanted to use the incomplete design to study an independent var iable with
seven levels, we would need to test 5,040 participants if we used all
possible orders —one participant for each of the possible orders of the
seven conditions (7! orders). We obviously need some alternative to using
all possible orders if we are to use the incomplete design for experiments
with fi ve or more conditions.
Practice effects can be balanced by using just some of all the possible
orders. The number of selected orders will always be equal to some
multiple of the number of conditions in t he experiment. For example, to do
an experiment with one independent variable with seven levels, we need to
select 7, 14, 21, 28, or some other multiple of seven orders to balance
practice effects.
3.3.6 The Problem of Differential Transfer :
• Differentia l transfer occurs when the effects of one condition persist
and influence performance in subsequent conditions.
• Variables that may lead to differential transfer should be tested using
a random groups design because differential transfer threatens the
internal validity of repeated measures designs.
• Differential transfer can be identified by comparing the results for the
same independent variable when tested in a repeated measures design
and in a random groups design. Researchers can overcome the
potent ial problem of practice effects in repeated measures designs by
using appropriate techniques to balance practice effects. There is a
much more serious potential problem that can arise in repeated
measures designs that is known as differential transfer (Pou lton, 1973,
1975, 1982; Poulton & Freeman, 1966). Differential transfer arises
when performance in one condition differs depending on the
condition that precedes it.
Consider a problem -solving experiment in which two types of instructions
are being compare d in a repeated measures design. One set of instructions munotes.in

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45 Research Methodology For Psychology (A) Is expected to enhance problem solving, whereas the other set of
instructions
(B) Serves as the neutral control condition.
It is reasonable to expect that participants tested in the order AB w ill be
unable or unwilling to abandon the approach outlined in the A instructions
when they are supposed to be following the B instructions. Giving up the
“good thing” participants had under instruction A would be the
counterpart of successfully following the admonition “Don’t think of pink
elephants!” When participants fail to give up the instruction from the first
condition (A) while they are supposed to be following instruction B, any
difference between the two conditions is reduced. For those participan ts,
after all, condition B was not really tried. The experiment becomes a
situation in which participants are tested in an “AA” condition, not an
“AB” condition.
In general, the presence of differential transfer threatens internal validity
because it becom es impossible to determine if there are true differences
between the conditions. It also tends to underestimate differences between
the conditions and thereby reduces the external validity of the findings.
Therefore, when differential transfer could occur, researchers should
choose an independent groups design. Differential transfer is sufficiently
common with instructional variables to advise against the use of repeated
measures designs for these studies (Underwood & Shaughnessy, 1975).
Unfortunately, diff erential transfer can arise in any repeated measures
design. For instance, the effect of 50 units of marijuana may be different if
administered after the participant has received 200 units than if
administered after the participant has received the placebo (e.g., if the
participant has an increased tolerance for marijuana after receiving the 200
dose). There are ways, however, to determine whether differential transfer
is likely to have occurred.
The best way to determine whether differential transfer is a problem is to
do two separate experiments (Poulton, 1982). The same independent
variable would be studied in both experiments, but a random groups
design would be used in one experiment and a repeated measures design in
the other. The random groups design cannot possibly involve differential
transfer because each participant is tested in only one condition. If the
experiment using a repeated measures design shows the same effect of the
independent variable as that shown in the random groups design, then
there has likely been no differential transfer. If the two designs show
different effects for the same independent variable, however, differential
transfer is likely to be responsible for producing the different outcome in
the repeated measures design. When d ifferential transfer does occur, the
results of the random groups design should be used to provide the best
description of the effect of the independent variable.
3.4 COMPLEX DESIGNS munotes.in

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46 Experimental And Quasi-Experimental Methods Complex designs can also be called factorial designs because they involv e
factorial combination of independent variables. Factorial combination
involves pairing each level of one independent variable with each level of
a second independent variable. This makes it possible to determine the
effect of each independent variable al one ( main effect ) and the effect of
the independent variables in combination ( interaction effect ).
Complex designs may seem a bit complicated at this point, but the
concepts will become clearer as you progress through this chapter. We
begin with a review o f the characteristics of experimental designs that can
be used to investigate independent variables in a complex design. We then
describe the procedures for producing, analyzing, and interpreting main
effects and interaction effects. We introduce the analy sis plans that are
used for complex designs. We conclude the chapter by giving special
attention to the interpretation of interaction effects in complex designs.
3.4.1 Describing effects in a complex design :
• Researchers use complex designs to study the effects of two or more
independent variables in one experiment.
• In complex designs, each independent variable can be studied with an
independent groups design or with a repeated measures design.
• The simplest complex design is a 2 x 2 design —two indep endent
variables, each with two levels.
• The number of different conditions in a complex design can be
determined by multiplying the number of levels for each independent
variable
• More powerful and efficient complex designs can be created by
includin g more levels of an independent variable or by including more
independent variables in the design.
An experiment with a complex design has, by definition, more than one
independent variable. Each independent variable in a complex design must
be implemente d using either an independent groups design or a repeated
measures design according to the procedures described in Chapters 6 and
7. When a complex design has both an independent groups variable and a
repeated measures variable, it is called a mixed design .
3.4.2 Main Effects and Interaction Effects :
• The overall effect of each independent variable in a complex design is
called a main effect and represents the differences among the average
performance for each level of an independent variable collapsed
across the levels of the other independent variable.
• An interaction effect between independent variables occurs when the
effect of one independent variable differs depending on the levels of
the second independent variable. munotes.in

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47 Research Methodology For Psychology In any complex factorial design it is possible to test predictions regarding
the overall effect of each independent variable in the experiment while
ignoring the effect of the other independent variable(s). The overall effect
of an independent variable in a complex design is called a ma in effect. We
will examine two main effects Kassin and his colleagues observed in their
experiment for two different dependent variables.
Prior to their interrogation of the suspect, student interrogators were given
information about interrogation techniqu es, including a list of possible
questions they could ask about the theft. Twelve questions were written as
pairs (but presented randomly in the list). One question of the pair was
written in such a way that the suspect’s guilt was presumed (e.g., “How
did you fi nd the key that was hidden behind the VCR?”) and the second
question in the pair was written so as not to presume guilt (e.g., “Do you
know anything about the key that was hidden behind the VCR?”). Student
interrogators were asked to select six que stions they might later want to
ask. Thus, students could select from 0 to 6 questions that presumed guilt.
Based on the behavioral confirmation theory, Kassin et al. predicted that
interrogators in the guilty -expectation condition would select more guilt -
presumptive questions than would interrogators in the innocent -
expectation condition. Thus, they predicted a main effect of the
interrogator -expectation independent variable.
The data for this dependent variable, number of guilt -presumptive
questions selec ted, are presented in Table 8.1. The overall mean number of
guilt presumptive questions for participants in the guilty -expectation
condition (3.62) is obtained by averaging the means of the actual -guilt and
actual -innocence conditions for interrogators in the guilty -expectation
condition: (3.54 + 3.70)/2 = 3.62. Similarly, the overall mean for the
innocent -expectation condition is computed to be 2.60: (2.54 + 2.66)/2 =
2.60.1 The means for a main effect represent the overall performance at
each level of a p articular independent variable collapsed across (averaged
over) the levels of the other independent variable. In this case we
collapsed (averaged) over the suspect status variable to obtain the means
for the main effect of the interrogator expectation vari able. The main
effect of the interrogator -expectation variable is the difference between the
means for the two levels of the variable (3.62 + 2.60 = 1.02). In the Kassin
et al. experiment, the main effect of the interrogator -expectation variable
indicates that the overall number of guilt -presumptive questions selected
was greater when interrogators expected a guilty suspect (3.62) than when
they expected an innocent suspect (2.60). Inferential statistics tests
confirmed that the main effect of interrogator expectation was statistically
significant. This supported the researchers’ hypothesis based on
behavioral confirmation theory.
Let’s now turn to a dependent variable for which there was a statistically
significant main effect of the suspect -status independ ent variable. The
researchers also coded the tape -recorded interviews to analyze the
techniques used by the interrogators to obtain a confession. Student
interrogators were given brief, written instructions regarding the powerful
techniques police use to b reak down a suspect’s resistance. Researchers munotes.in

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48 Experimental And Quasi-Experimental Methods counted the number of interrogator statements that reflected these
persuasive techniques, such as building rapport, assertions of the suspect’s
guilt or disbelief in the suspect’s statements, appeals to the sus pect’s self -
interest or conscience, threats of punishment, promises of leniency, and
presentation of false evidence.
3.4.3 Complex Designs with Three Independent Variables :
The power and complexity of complex designs increase substantially
when the number of independent variables in the experiment increases
from two to three. In the two -factor design there can be only one
interaction effect, but in the three factor design each independent variable
can interact with each of the other two independent variable s and all three
independent variables can interact together.
Thus, the change from a two -factor to a three -factor design introduces the
possibility of obtaining four different interaction effects. If the three
independent variables are symbolized as A, B, and C, the three -factor
design allows a test of the main effects of A, B, and C; two -way
interaction effects of A x B, A x C, B x C; and the three -way interaction
effect of A x B x C. The efficiency of an experiment involving three
independent variables is remarkable. An experiment investigating
discrimination in the workplace will give you a sense of just how powerful
complex designs can be. Pingitore, Dugoni, Tindale, and Spring (1994)
investigated possible discrimination against moderately obese people i n a
mock job interview. Participants in the experiment viewed videotapes of
job interviews. In one of their experiments they used a 2 x 2 x 2 design.
The first independent variable was the weight of the applicant (normal or
overweight). The role of the app licant for the job in the videotapes was
played by professional actors who were of normal weight. In the
moderately obese conditions, the actors wore makeup and prostheses so
that they appeared 20% heavier. The second independent variable in the
experiment was the sex of the applicant (male or female). The third
independent variable was participants’ concern about their own body and
the importance of body awareness to their self -concept (high or low). This
variable was defined using a self -report measure of how participants
viewed their body.
A natural groups design was used to study this “body -schema variable.”
Participants were randomly assigned to evaluate male or female applicants
who were normal weight or moderately obese (random groups designs).
The de pendent variable was the participants’ rating on a 7 -point scale of
whether they would hire the applicant (1 = definitely not hire and 7 =
definitely hire). The results of the Pingitore et al. experiment for these
three variables are shown in Figure 8.5. A s you can see, displaying the
means for a three -variable experiment requires a graph with more than one
“panel.” One panel of the figure shows the results for two variables at one
level of the third variable, and the other panel shows results for the same
two variables at the second level of the third independent variable.
3.4.4 Interaction Effects and Ceiling and Floor Effects : munotes.in

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49 Research Methodology For Psychology When participants’ performance reaches a maximum (ceiling) or a
minimum (floor) in one or more conditions of an experiment, result s for
an interaction effect are uninterpretable.
Consider the results of a 3 x 2 experiment investigating the effects of
increasing amounts of practice on performance during a physical -fitness
test. There were six groups of participants in this plausible b ut
hypothetical experiment. Participants were first given 10, 30, or 60
minutes to practice, doing either easy or hard exercises. Then they took a
fi tness test using easy or hard exercises (the same they had practiced).
The dependent variable was the perc entage of exercises that each
participant was able to complete in a 15 -minute test period.
The pattern of results in Figure 8.7 looks like a classic interaction effect;
the effect of amount of practice time differed for the easy and hard
exercises. Increas ing practice time improved test performance for the hard
exercises, but performance leveled off after 30 minutes of practice with
the easy exercises. If a standard analysis was applied to these data, the
interaction effect would very likely be statisticall y significant.
Unfortunately, this interaction effect would be essentially uninterpretable.
For those groups given practice with the easy exercises, performance
reached the maximum level after 30 minutes of practice, so no
improvement beyond this point cou ld be shown in the 60 -minute group.
Even if the participants given 60 minutes of practice had further benefited
from the extra practice, the experimenter could not measure this
improvement on the chosen dependent variable.
The preceding experiment illustra tes the general measurement problem
referred to as a ceiling effect. Whenever performance reaches a maximum
in any condition of an experiment, there is danger of a ceiling effect. The
corresponding name given to this problem when performance reaches a
minimum (e.g., zero errors on a test) is a floor effect. Researchers can
avoid ceiling and floor effects by selecting dependent variables that allow
ample “room” for performance differences to be measured across
conditions. For example, in the fitness experime nt it would have been
better to test participants with a greater number of exercises than anyone
could be expected to complete in the time allotted for the test. The mean
number of exercises completed in each condition could then be used to
assess the effe cts of the two independent variables without the danger of a
ceiling effect. It is important to note that ceiling effects also can pose a
problem in experiments that don’t involve a complex design. If the fitness
experiment had included only the easy exerc ises, there would still be a
ceiling effect in the experiment.
3.5 QUASI -EXPERIMENTAL DESIGNS AND PROGRAM EVALUATION There are many reasons why researchers do experiments in natural
settings. One reason for these “field experiments” is to test the externa l
validity of a laboratory finding (see Chapter 6). That is, we seek to fi nd
out if a treatment effect observed in the laboratory works in a similar way munotes.in

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50 Experimental And Quasi-Experimental Methods in another setting. Other reasons for experimenting in natural settings are
more practical.
Research i n natural settings is likely to be associated with attempts to
improve conditions under which people live and work. The government
may experiment with a new tax system or a new method of job training for
the economically disadvantaged. Schools may experime nt by changing
lunch programs, after -school care, or curricula. A business may
experiment with new product designs, methods of delivering employee
benefits, or flexible work hours. In these cases, as is true in the laboratory,
it is important to determine whether the “treatment” caused a change. Did
a change in the way patients are admitted to a hospital emergency room
cause patients to be treated more quickly and efficiently? Did a college
energy conservation program cause a decrease in energy consumption?
Knowing whether a treatment was effective permits us to make important
decisions about continuing the treatment, about spending additional
money, about investing more time and effort, or about changing the
present situation on the basis of our knowledge o f the results.
Research that seeks to determine the effectiveness of changes made by
institutions, government agencies, and other organizations is one goal of
program evaluation.
• Quasi -experiments provide an important alternative when true
experiments a re not possible.
• Quasi -experiments lack the degree of control found in true
experiments; most notably, quasi -experiments typically lack random
assignment.
• Researchers must seek additional evidence to eliminate threats to
internal validity when they d o quasi -experiments rather than true
experiments.
• The one -group pretest -posttest design is called a pre -experimental
design or a bad experiment because it has so little internal validity.
A dictionary will tell you that one definition of the prefix quas i- is
“resembling.” Quasi -experiments involve procedures that resemble those
of true experiments. Generally speaking, quasi -experiments include some
type of intervention or treatment and they provide a comparison, but they
lack the degree of control found in true experiments. Just as randomization
is the hallmark of true experiments, so lack of randomization is the
hallmark of quasi experiments. As Campbell and Stanley (1966) explain,
quasi -experiments arise when researchers lack the control necessary to
perform a true experiment.
Quasi -experiments are recommended when true experiments are not
feasible. Some knowledge about the effectiveness of a treatment is more
desirable than none. The list of possible threats to internal validity that we
reviewed earlier can be used as a checklist in deciding just how good that
knowledge is. Moreover, the investigator must be prepared to look for munotes.in

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51 Research Methodology For Psychology additional kinds of evidence that might rule out a threat to internal validity
that is not specifically controlled in a quasi -experiment. For example,
suppose that a quasi -experiment does not control for history threats that
would be eliminated by a true experiment.
The investigator may be able to show that the history threat is implausible
based on a logical analysis of the situa tion or based on evidence provided
by a supplementary analysis. If the investigator can show that the history
threat is implausible, then a stronger argument can be made for the
internal validity of the quasi -experiment. Researchers must recognize the
specifi c shortcomings of quasi -experimental procedures, and they must
work like detectives to provide whatever evidence they can to overcome
these shortcomings. As we begin to consider the appropriate uses of quasi -
experiments, we need to acknowledge that the re is a great difference
between the power of the true experiment and that of the quasi -
experiment. Before facing the problems of interpretation that result from
quasi -experimental procedures, the researcher should make every effort
possible to approximate the conditions of a true experiment.
Perhaps the most serious limitation researchers face in doing experiments
in natural settings is that they are frequently unable to assign participants
randomly to conditions. This occurs, for instance, when an intact group is
singled out for treatment and when administrative decisions or practical
considerations prevent randomly assigning participants. For example,
children in one classroom or school and workers at a particular plant
represent intact groups that might receive a treatment or intervention
without the possibility of randomly assigning individuals to conditions. If
we assume that behavior of a group is measured both before and after
treatment, such an “experiment” can be described as follows:
O1 X O2 :
Where O1 refers to the fi rst observation of a group, or pretest, X indicates
a treatment, and O2 refers to the second observation, or posttest. This one -
group pretest -posttest design represents a pre -experimental design or,
more simply, may be called a bad exp eriment. Any obtained difference
between the pretest and posttest scores could be due to the treatment or to
any of several threats to internal validity, including history, maturation,
testing, and instrumentation threats (as well as experimenter expectanc y
effects and novelty effects). The results of a bad experiment are
inconclusive with respect to the effectiveness of a treatment. Fortunately,
there are quasi -experiments that improve upon this pre -experimental
design.
3.5.1 The Nonequivalent Control Grou p Design :
• In the nonequivalent control group design, a treatment group and a
comparison group are compared using pretest and posttest measures.
If the two groups are similar in their pretest scores prior to treatment
but differ in their posttest scores f ollowing treatment, researchers can
more confidently make a claim about the effect of treatment. munotes.in

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52 Experimental And Quasi-Experimental Methods • Threats to internal validity due to history, maturation, testing,
instrumentation, and regression can be controlled in a nonequivalent
control group design.
The one -group pretest -posttest design can be modified to create a quasi
experimental design with greatly superior internal validity if two
conditions are met: (1) there exists a group “like” the treatment group that
can serve as a comparison group, and (2 ) there is an opportunity to obtain
pretest and posttest measures from individuals in both the treatment and
the comparison groups. Campbell and Stanley (1966) call a quasi -
experimental procedure that meets these two conditions a nonequivalent
control grou p design. Because a comparison group is selected on bases
other than random assignment, we cannot assume that individuals in the
treatment and control groups are equivalent on all important
characteristics (i.e., a selection threat arises). Therefore, it i s essential that
a pretest be given to both groups to assess their similarity on the
dependent measure. A nonequivalent control group design can be outlined
as follows:
O1 X O2
- - - - - -
O1 O2
The dashed line indicates that the treatment and comparison g roups were
not formed by assigning participants randomly to conditions.
By adding a comparison group, researchers can control threats to internal
validity due to history, maturation, testing, instrumentation, and
regression. A brief review of the logic of experimental design will help
show why this occurs. We wish to begin an experiment with two similar
groups; then one group receives the treatment and the other does not. If the
two groups’ posttest scores differ following treatment, we fi rst must rule
out alternative explanations before we can claim that treatment caused the
difference. If the groups are truly comparable, and both groups have
similar experiences (except for the treatment), then we can assume that
history, maturation, testing, instrumentati on, and regression effects occur
to both groups equally. Thus, we may assume that both groups change
naturally at the same rate (maturation), experience the same effect of
multiple testing, or are exposed to the same external events (history). If
these eff ects are experienced in the same way by both groups, they cannot
possibly be used to account for group differences on posttest measures.
Therefore, they no longer are threats to internal validity. Thus, researchers
gain atremendous advantage in their abili ty to make causal claims simply
by adding a comparison group. These causal claims, however, depend
critically on forming comparable groups at the start of the study, and
ensuring that the groups then have comparable experiences, except for the
treatment. B ecause this is difficult to realize in practice, as we’ll see,
threats to internal validity due to additive effects with selection typically
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53 Research Methodology For Psychology As you approach the end of a course on research methods in psychology,
you might appreciate learning about the results of a nonequivalent control
group design that examined the effect of taking a research methods course
on reasoning about real -life events (Vander Stoep & Shaughnessy, 1997).
Students enrolled in two sections of a resea rch methods course (and who
happened to be using an edition of this textbook) were compared with
students in two sections of a developmental psychology course on their
performance on a test emphasizing methodological reasoning about
everyday events. Studen ts in both kinds of classes were administered tests
at the beginning and at the end of the semester. Results revealed that
research methods students showed greater improvement than did students
in the control group. Taking a research methods course improve d students’
ability to think critically about real -life events.
With that bit of encouraging news in mind, let us now examine in detail
another study using a nonequivalent control group design. This will give
us the opportunity to review both the specific strengths and limitations of
this quasi experimental procedure.
Sources of Invalidity in the Nonequivalent Control Group Design :
• To interpret the findings in quasi -experimental designs, researchers
examine the study to determine if any threats to intern al validity are
present.
• The threats to internal validity that must be considered when using the
nonequivalent control group design include additive effects with
selection, differential regression, observer bias, contamination, and
novelty effects.
• Although groups may be comparable on a pretest measure, this does
not ensure that the groups are comparable in all possible ways that are
relevant to the outcome of the study.
3.5.2 Interrupted Time -Series Designs :
• In a simple interrupted time -series des ign, researchers examine a
series of observations both before and after a treatment.
• Evidence for treatment effects occurs when there are abrupt changes
(discontinuities) in the time -series data at the time treatment was
implemented.
• The major threat s to internal validity in the simple interrupted time -
series design are history effects and changes in measurement
(instrumentation) that occur at the same time as the treatment.
A second quasi -experiment, a simple interrupted time -series design, is
possib le when researchers can observe changes in a dependent variable for
some time before and after a treatment is introduced (Shadish et al., 2002).
The essence of this design is the availability of periodic measures before
and after a treatment has been intro duced. The simple interrupted time -
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54 Experimental And Quasi-Experimental Methods O1 O2 O3 O4 O5 X O6 O7 O8 O9 O10
3.5.3 Program evaluation :
• Program evaluation is used to assess the effectiveness of human
service organizations and provide feedback to administrators about
their services.
• Program evaluators assess needs, process, outcome, and efficiency of
social services.
• The relationship between basic research and applied research is
reciprocal.
• Despite society’s reluctance to use experimen ts, true experiments and
quasi experiments can provide excellent approaches for evaluating
social reforms.
Organizations that produce goods have a ready -made index of success. If a
company is set up to make microprocessors, its success is ultimately
determ ined by its profi ts from the sale of microprocessors. At least
theoretically, the efficiency and effectiveness of the organization can be
easily assessed by examining the company’s financial ledgers.
Increasingly, however, organizations of a different sor t play a critical role
in our society. Because these organizations typically provide services
rather than goods, Posavac (2011) refers to them as human service
organizations. For example, hospitals, schools, police departments, and
government agencies prov ide a variety of services ranging from
emergency room care to fi re prevention inspections. Because profit -
making is not their goal, some other method must be found to distinguish
between effective and ineffective agencies. One useful approach to
assessin g the effectiveness of human service organizations is program
evaluation.
According to Posavac (2011), program evaluation is: a methodology to
learn the depth and extent of need for a human service and whether the
service is likely to be used, whether the service is sufficiently intensive to
meet the unmet needs identified, and the degree to which the service is
offered as planned and actually does help people in need at a reasonable
cost without unacceptable side effects. (p. 1)
The definition of program e valuation includes several components; we
will take up each of these components in turn. Posavac emphasizes,
however, that the overarching goal of program evaluation is to provide
feedback regarding human service activities. Program evaluations are
designe d to provide feedback to the administrators of human service
organizations to help them decide what services to provide to whom and
how to provide them most effectively and efficiently. Program evaluation
is an integrative discipline that draws on politica l science, sociology,
economics, education, and psychology. We are discussing program
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55 Research Methodology For Psychology it represents perhaps the largest -scale application of the principles and
methods we have be en describing throughout this book.
Posavac (2011) identifies four questions that are asked by program
evaluators. These questions are about needs, process, outcome, and effi
ciency. An assessment of needs seeks to determine the unmet needs of the
people f or whom an agency might provide a service. Consider, for
example, a city government that has received a proposal to institute a
program of recreational activities for senior citizens in the community.
The city would fi rst want to determine whether senior citizens actually
need or want such a program. If the senior citizens do want such a
program, the city would further want to know what kind of program would
be most attractive to them. The methods of survey research are used
extensively in studies designed to assess needs. Administrators can use the
information obtained from an assessment of needs to help them plan what
programs to offer. Once a program has been set up, program evaluators
may ask questions about the process that has been established.
Observ ational methods are often useful in assessing the processes of a
program.
Programs are not always implemented the way they were planned, and it is
essential to know what actually is being done when a program is
implemented. If the planned activities were not being used by the senior
citizens in a recreational program designed specifi cally for them, it might
suggest that the program was inadequately implemented. An evaluation
that provides answers to questions about process, that is, about how a
program is actually being carried out, permits administrators to make
adjustments in the delivery of services in order to strengthen the existing
program (Posavac, 2011).
The next set of questions a program evaluator is likely to ask involves the
outcome. Has the pr ogram been effective in meeting its stated goals? For
example, do senior citizens now have access to more recreational
activities, and are they pleased with these activities? Do they prefer these
particular activities over other activities? The outcome of a neighborhood -
watch program designed to curb neighborhood crime might be evaluated
by assessing whether there were actual decreases in burglaries and assaults
following the implementation of the program. It is possible to use archival
data like those desc ribed in Chapter 4 to carry out evaluations of outcome.
For example, examining police records in order to document the frequency
of various crimes is one way to assess the effectiveness of a
neighborhood -watch program. Evaluations of outcome may also invol ve
both experimental and quasi -experimental methods for research in natural
settings. An evaluator may, for example, use a nonequivalent control
group design to assess the effectiveness of a school reform program by
comparing students’ performance in two d ifferent school districts, one
with the reform program and one without. The final questions evaluators
might ask are about the efficiency of the program.
Most often, questions about efficiency relate to the cost of the program.
Choices often have to be mad e among possible services that a government munotes.in

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56 Experimental And Quasi-Experimental Methods or other institution is capable of delivering. Information about how
successful a program is (outcome evaluation) and information about the
program’s cost efficiency evaluation) are necessary if we want to make
informed decisions about continuing the program, how to improve it,
whether to try an alternative program, or whether to cut back on the
program’s services.
3.6 REFERENCES 1. Shaughnessy, J. J., Zechmeister, E. B. &Zechmeister, J. (2012).
Research methods in psychology . (9th ed..). NY: McGraw Hill.
2. Elmes, D. G. (2011). Research Methods in Psychology (9thed.).
Wadsworth Publishing.
3. Goodwin, J. (2009). Research in Psychology: Methods in Design
(6thed.). Wiley.
4. McBurney, D. H. (2009). Research m ethods . (8th Ed.). Wadsworth
Publishing.
5. Forrester, M. A. (2010). Doing Qualitative Research in Psychology: A
Practical Guide . Sage.

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57
4
QUALITATIVE RESEARCH
Unit Structure
4.1 Introduction
4.2 Philosophy and conceptual foundations; proposing and reporting
qualitative research
4.2.1 Philosophy And Conceptual Foundations
4.2.2. Proposing and reporting Qualitative research
4.3 Grounded theory
4.3.1 Benefits of using grounded theory
4.3.2 Limitations of grounded theory
4.4 Interpretive phenomenological analysis; discourse analysis
4.4.1 Interpretative phenomenological analysis (IPA)
4.4.2 Discourse analysis
4.5 Narrative analysis; co nversation analysis
4.6 References
4.1 INTRODUCTION Qualitative research is defined as a market research method that focuses
on obtaining data through open -ended and conversational communication.
This method is not only about “what” people think but also “why” they
think so. For example, consider a convenience store looking to improve its
patronage. A systematic observation concludes that the number of men
visiting this store are more. One good method to determine why women
were not visiting the store is to conduct an in -depth interview of potential
customers in the category.
For example, on successfully interviewing female customers, visiting the
nearby stores and malls, and selecting them through random sampling, it
was known that the store doesn’t have enough items for women and so
there were fewer women visiting the store, which was understood only by
personally interacting with them and understanding why they didn’t visit
the store, because there were more male products than female ones.
Qualitative re search is based on the disciplines of social sciences like
psychology, sociology, and anthropology. Therefore, the qualitative
research methods allow for in -depth and further probing and questioning
of respondents based on their responses, where the interv iewer/researcher
also tries to understand their motivation and feelings. Understanding how
your audience takes decisions can help derive conclusions in market
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58 Qualitative Research 4.2 PHILOSOPHY AND CONCEPTUAL FOUNDATIONS; PROPOSING AND REPORTING
QUALITATIVE RESEARCH 4.2.1 Philosophy And Conceptual Foundations :
All knowledge production is based on a set of philosophical assumptions
about the nature of reality (ontology), the nature of knowledge
(epistemology), and the ways in which we acquire knowledge
(methodology). These are known as paradigms, or worldviews (Kuhn,
1970). They refer to researchers’ assumptions about the world and are
often implicit or taken for granted. Paradigm assumptions include claims
about notions such as subjectivity, objectivity, truth, knowl edge, and
reality. Paradigms inform the kinds of questions that can be asked and
answered through research – and those that cannot. Paradigms guide both
the researcher and the research inquiry (Kuhn, 1970; Guba & Lincoln,
1994).
For example, a post -positiv ist qualitative researcher may favour results
presented as description to better fit with the dominant ideas of the post -
positivist paradigm. An assumption of this paradigm is that the researcher
should stay as close as possible to participants’ words and their
descriptions of events. Interpretive qualitative researchers, on the other
hand, would explicitly engage with interpretation throughout the study
process. They want to not only describe a phenomenon but to also explain
analytical insights they have g leaned about it through the study.
Interpretive researchers might develop a new concept based on a
theoretically informed analysis and interpretation of participants’ words
and their descriptions of events. They might offer an explanation that is
analytica lly or conceptually generalizable beyond the study itself and
possibly transferable to other contexts, such as the “discourse of abuse” in
Eakin (2005) or the concept of “talk” in Facey (2010).
Researchers can identify their knowledge -producing paradigms or
worldview by thinking about whether they acquire knowledge by being
“objective” and “unbiased,” by being detached, value -free observers, or by
acknowledging their subjectivity. They can also consider whether they see
themselves as intimately involved i n co -producing knowledge, whether
they think the research process and the knowledge produced is, or can be,
value -free; and whether they can know and produce knowledge about how
things really are and how they really work.
Guba and Lincoln (Guba & Lincoln, 1994) propose four paradigms:
positivism, post -positivism, critical social, and
constructivism/interpretivism. Both (post -)positivist paradigms assume
that a stable reality exists “out there,” that phenomena such as health and
disease exist whether we loo k for or find them or not, and that what exists
as health and disease are real only if they can be observed through or are
amenable to the senses. “Stable” means for example that realities such as
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59 Research Methodology For Psychology political, historical, or economic processes; only what is observable can be
considered valid, and knowledge is achieved through the accumulation of
verified facts.
From a (post -)positivist perspective, metaphysical notions such as one’s
feelings would be considered valid knowledge only if they could be
observed or measured (Guba & Lincoln, 1994; Green & Thorogood, 2004;
Denzin & Lincoln, 2011). This philosophy also holds that researchers
must be objective, which means they must rid themselve s of their biases
because these can taint the research process and thus undermine the
validity of the knowledge produced. This orientation is more appropriate
for research in the natural sciences. In the health sciences, it focuses on
prediction of behavio ur and functionalist frameworks to explain social
relations.
The second paradigm that is very influential in the health sciences is the
critical -social paradigm. In this paradigm, reality is shaped by socio -
economic, political, historical, and cultural con texts. Researchers
acknowledge their subjectivity and as a result, recognize that truths (e.g.,
research findings) are value -mediated (Guba & Lincoln, 1994). Critical -
social theories are concerned with issues of power – underlying power
structures and how they impinge on individuals and groups. Within this
paradigm, theories such as neo -Marxism, feminism, postcolonialism,
poststructuralism, postmodernism, and critical race studies, among others,
explore the power relations that shape current social relation s. Researchers
are involved in advocacy and committed to social justice (Guba and
Lincoln, 1994). Their objective is to produce knowledge to promote social
change by identifying forms of oppression and supporting the
empowerment of disadvantaged groups (De nzin, 2015). They study how
we came to have groups of privileged people benefiting from the current
power arrangements while others experience unnecessary suffering and
deprivation. For example, why does the nursing profession have less social
prestige and remuneration than the medical profession despite providing
essential health care? Or, why is there a lack of access to dental care for
part-time workers and their families?
Constructivism/constructionism/interpretivism lies at the other end of the
continu um in Guba and Lincoln’s (1994) typology of paradigms. The
theories organized under this paradigm that are better known in the health
sciences are phenomenology, social constructionism, and symbolic
interactionism. This perspective assumes that reality is multiple,
contingent, and socially constructed through social interactions. And,
unlike (post -)positivism, it has the capacity to include metaphysical
considerations. Interpretivism is concerned with meaning and subjective
experiences, with understanding p henomena from the perspective of those
who experience it (Green & Thorogood, 2004).
Further, where (post -)positivist researchers assert that knowledge and
understanding of health and diseases are products of accumulated facts,
constructivist/ interpretivis t researchers argue that they are social
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60 Qualitative Research informed by social, historical, and political contexts (Singer, 2004). For
example, TB sufferers in Canada experience their disease as stigma,
depression, fear, isolation, and anxiety; as a limitation on their freedom
and autonomy; and as an intrusion that is related to surveillance through
Directly Observed Therapy (DOT) programs (Bender, 2009; Bender,
Peter, Wynn, Andrews & Pringle, 2011). A con structivist/interpretivist
researcher would note that their experiences are shaped in part by their
social status as new and/or racialized immigrants, the construction of TB
as contagion, and the personal moral judgments that inform such
understandings of this disease (Bender, Guruge, Hyman & Janjua, 2012).
The assertion that diseases are “social constructions” does not mean they
do not exist. Diseases objectively do exist, but this perspective prompts us
to carefully consider the ways in which we think and talk about them. As
the TB example above suggests, prevailing attitudes toward a particular
disease have implications for the people diagnosed with that disease.
Also, in this paradigm, the researcher’s values and roles hold primary
places in the research process; the researcher is the “orchestrator and
facilitator of the inquiry” (Guba & Lincoln, 1994; Denzin & Lincoln,
2011). The researcher and the participant are also inexorably linked in a
research relationship. This means, for example, that the resear ch data and
by extension research results are co -created in the research process. From
this perspective, researchers do not make claims of objectivity, but rather
acknowledge and engage their thoughts and feelings during the research
process. They “account for themselves” through the ethical and
epistemological lens of reflexivity (Denzin and Lincoln, 2011). These
reflexive practices not only become a resource that informs the research
inquiry and outcomes, they also buttress the rigour or quality of resear ch
because they contribute to transparency in research practice and process.
4.2.2. Proposing and reporting Qualitative research :
Writing the proposal of a research work in the present era is a challenging
task due to the constantly evolving trends in the qualitative research
design and the need to incorporate medical advances into the
methodology. The proposal is a detailed plan or ‘blueprint’ for the
intended study, and once it is completed, the research project should flow
smoothly. Even today, many of t he proposals at post -graduate evaluation
committees and application proposals for funding are substandard.
A proposal needs to show how your work fits into what is already known
about the topic and what new paradigm will it add to the literature, while
specifying the question that the research will answer, establishing its
significance, and the implications of the answer. The proposal must be
capable of convincing the evaluation committee about the credibility,
achievability, practicality and reproducibili ty (repeatability) of the
research design. Four categories of audience with different expectations
may be present in the evaluation committees, namely academic
colleagues, policy -makers, practitioners and lay audiences who evaluate
the research proposal. T ips for preparation of a good research proposal munotes.in

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61 Research Methodology For Psychology include; ‘be practical, be persuasive, make broader links, aim for crystal
clarity and plan before you write’.
A researcher must be balanced, with a realistic understanding of what can
be achieved. Being per suasive implies that researcher must be able to
convince other researchers, research funding agencies, educational
institutions and supervisors that the research is worth getting approval.
The aim of the researcher should be clearly stated in simple langua ge that
describes the research in a way that non -specialists can comprehend,
without use of jargons. The proposal must not only demonstrate that it is
based on an intelligent understanding of the existing literature but also
show that the writer has though t about the time needed to conduct each
stage of the research.
Reporting of qualitative research results should identify the main analytic
findings. Often, these findings involve interpretation and
contextualization, which represent a departure from the tr adition in
quantitative studies of objectively reporting results. The presentation of
results often varies with the specific qualitative approach and
methodology; thus, rigid rules for reporting qualitative findings are
inappropriate. However, authors shou ld provide evidence (e.g., examples,
quotes, or text excerpts) to substantiate the main analytic findings.
Qualitative research is expansive and occasionally controversial, spanning
many different methods of inquiry and epistemological approaches. A
“one-size-fits-all” standard for reporting qualitative research can be
restrictive, but COREQ and SRQR both serve as valuable tools for
developing responsible qualitative research proposals, effectively
communicating research decisions, and evaluating submission s.
Ultimately, tailoring a set of standards specific to health design research
and its frequently used methods would ensure quality research and aid
reviewers in their evaluations.
4.3 GROUNDED THEORY “Grounded theory refers to a set of systematic inducti ve methods for
conducting qualitative research aimed toward theory development. The
term grounded theory denotes dual referents: (a) a method consisting of
flexible methodological strategies and (b) the products of this type of
inquiry. Increasingly, resea rchers use the term to mean the methods of
inquiry for collecting and, in particular, analyzing data. The
methodological strategies of grounded theory are aimed to construct
middle -level theories directly from data analysis. The inductive theoretical
thrus t of these methods is central to their logic. The resulting analyses
build their power on strong empirical foundations. These analyses provide
focused, abstract, conceptual theories that explain the studied empirical
phenomena.
Grounded theory has consider able significance because it (a) provides
explicit, sequential guidelines for conducting qualitative research; (b)
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62 Qualitative Research streamlines and integrates data collection and analysis; (d) adva nces
conceptual analysis of qualitative data; and (e) legitimizes qualitative
research as scientific inquiry. Grounded theory methods have earned their
place as a standard social research method and have influenced researchers
from varied disciplines and p rofessions.
Yet grounded theory continues to be a misunderstood method, although
many researchers purport to use it. Qualitative researchers often claim to
conduct grounded theory studies without fully understanding or adopting
its distinctive guidelines. They may employ one or two of the strategies or
mistake qualitative analysis for grounded theory. Conversely, other
researchers employ grounded theory methods in reductionist, mechanistic
ways. Neither approach embodies the flexible yet systematic mode of
inquiry, directed but open -ended analysis, and imaginative theorizing from
empirical data that grounded theory methods can foster. Subsequently, the
potential of grounded theory methods for generating middle -range theory
has not been fully realized.”
You s hould consider using grounded theory when there is no existing
theory that offers an explanation for a phenomenon that you are studying.
It can also be used if there is an existing theory, but it is potentially
incomplete as the data used to derive that th eory wasn’t collected from the
group of participants that you plan on researching.
4.3.1 Benefits of using grounded theory :
Findings accurately represent real world settings :
The theories you develop using grounded theory are derived directly from
real wor ld participants in real world settings using methods like in depth
interviews and observation, so your findings will more accurately
represent the real world. This is in contrast to other research approaches
that occur in less natural settings like researc h labs or focus group tables.
Findings are tightly connected to the data :
Because grounded theory primarily relies on collected data to determine
the final outcome, the findings are tightly connected to that data. This is in
contrast to other research app roaches that rely more heavily on external
research frameworks or theories that are further removed from the data.
Great for new discoveries:
Grounded theory is a strong, inductive research method for discovering
new theories. You don’t go in with any prec onceived hypothesis about the
outcome, and are not concerned with validation or description. Instead,
you allow the data you collect to guide your analysis and theory creation,
leading to novel discoveries.
Offers strategies for analysis :
The process of gr ounded theory describes specific strategies for analysis
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63 Research Methodology For Psychology methodology, the analysis strategies enable you to stay structured and
analytical in your discovery process.
Data collection and analysis are streamlined :
Data collection and analysis are tightly interwoven. As you collect data,
you analyze it, and as you learn from analysis, you continue to collect
more data. This helps ensure that the data you collect is sufficient enough
to explain the findings that arise from analysis.
Buffers against confirmation bias :
Because data collection and analysis are tightly interwoven, you are truly
following what is emerging from the data itself. This provides a great
buffer against confirming pr econceived beliefs about your topic.
4.3.2 Limitations of grounded theory :
Difficulty recruiting :
Grounded theory relies on an iterative recruiting process called theoretical
sampling where you continuously recruit and conduct new rounds of
interviews with new participants and previous participants while you
analyze data. The recruiting criteria also evolves and changes based on
what you learn. Because the recruiting is not predefined, it can be
challenging to continuously find the right participants for yo ur study.
Time consuming to collect data :
There is no way to know ahead of time how much data you will need to
collect, so you need to be flexible with your time. With grounded theory,
you continuously collect and analyze data until you reach theoretical
saturation, which is the point at which new data does not contribute new
insight to your evolving theory. This means that you are likely to conduct
many rounds of data collection before your theory is complete.
Challenges in analysis:
Data analysis occurs on a rolling basis and involves making constant
comparisons between different excerpts of data. It can be challenging to
keep track of your comparisons and findings as you go. It can be helpful to
use a qualitative data analysis software like Delve to help you stay
organized during your analysis.
Steps for grounded theory :
1. Determine initial research questions
2. Recruit and collect data (theoretical sampling)
3. Break transcripts into excerpts (open coding)
4. Group excerpts into codes (open coding)
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64 Qualitative Research 6. Analyze more excerpts and compare with codes
7. Repeat steps 2 -6 until you reach theoretical saturation
8. Define the central idea (selective coding)
9. Write your grounded theory
Grounded theory is not a linea r process where you collect data, analyze it,
and then you’re done. It is an iterative research methodology that involves
cycling through the steps iteratively. Part of what made Grounded Theory
revolutionary was that it mixed data collection with analysis . It
emphasized going back to the field even after conducting some analysis.
You will recruit some participants, gather data and analyse it, and go back
into the field again with a different recruiting strategy and focus of
inquiry. Then you’ll incorporate those findings into further rounds of
analysis. Grounded theory is deliberately cyclical in nature.
4.4 INTERPRETIVE PHENOMENOLOGICAL ANALYSIS; DISCOURSE ANALYSIS 4.4.1 Interpretative phenomenological analysis (IPA):
Interpretative phenomenological analy sis (IPA) is an approach to
psychological qualitative research with an idiographic focus, which means
that it aims to offer insights into how a given person, in a given
context, makes sense of a given phenomenon. Usually, these phenomena
relate to experie nces of some personal significance, such as a major life
event, or the development of an important relationship. It has its
theoretical origins in phenomenology and hermeneutics, and key ideas
from Edmund Husserl, Martin Heidegger, and Maurice Merleau -Ponty are
often cited. IPA is one of several approaches to
qualitative, phenomenological psychology. It is distinct from other
approaches, in part, because of its combination of psychological,
interpretative, and idiographic components.
Sometimes IPA studies i nvolve a close examination of the experiences
and meaning -making activities of only one participant. Most frequently
they draw on the accounts of a small number of people (6 has been
suggested as a good number, although anywhere between 3 and 15
participan ts for a group study can be acceptable . In either case,
participants are invited to take part precisely because they can offer the
researcher some meaningful insight into the topic of the study; this is
called purposive sampling [i.e. it is not randomised] . Usually, participants
in an IPA study are expected to have certain experiences in common with
one another: the small -scale nature of a basic IPA study shows how
something is understood in a given context, and from a shared perspective,
a method sometimes called homogeneous sampling. More advanced IPA
study designs may draw together samples that offer multiple perspectives
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65 Research Methodology For Psychology and patients); or they may collect accounts over a period of time, to
develop a longitudinal analysis.
Data collection :
In IPA, researchers gather qualitative data from research participants using
techniques such as interview, diaries, or focus group. Typically, these are
approached from a position of flexible and o pen-ended inquiry, and the
interviewer adopts a stance that is curious and facilitative (rather than, say,
challenging and interrogative). IPA usually requires personally salient
accounts of some richness and depth, and it requires that these accounts be
captured in a way that permits the researcher to work with a detailed
verbatim transcript.
Data analysis :
Data collection does not set out to test hypotheses, and this stance is
maintained in data analysis. The analyst reflects upon their own
preconceptions about the data, and attempts to suspend these in order to
focus on grasping the experiential world of the research participant.
Transcripts are coded in considerable detail, with the focus shifting back
and forth from the key claims of the participant, to the researcher’s
interpretation of the meaning of those claims. IPA’s hermeneutic stance is
one of inquiry and meaning -making, and so the analyst attempts to make
sense of the participant’s attempts to make sense of their own experiences,
thus creating a double hermeneutic. One might use IPA if one had a
research question which aimed to understand what a given experience was
like (phenomenology) and how someone made sense of it (interpretation).
Analysis in IPA is said to be ‘bottom -up.’ This means that th e researcher
generates codes from the data, rather than using a pre -existing theory to
identify codes that might be applied to the data. IPA studies do not test
theories, then, but they are often relevant to the development of existing
theories. One might use the findings of a study on the meaning of sexual
intimacy to gay men in close relationships, for example, to re -examine the
adequacy of theories which attempt to predict and explain safe sex
practices. IPA encourages an open -ended dialogue between the researcher
and the participants and may, therefore, lead us to see things in a new
light.
After transcribing the data, the researcher works closely and intensively
with the text, annotating it closely (‘coding’) for insights into the
participants’ experien ce and perspective on their world. As the analysis
develops, the researcher catalogues the emerging codes, and subsequently
begins to look for patterns in the codes. These patterns are called ‘themes’.
Themes are recurring patterns of meaning (ideas, thoug hts, feelings)
throughout the text. Themes are likely to identify both something
that matters to the participants (i.e. an object of concern, topic of some
import) and also convey something of the meaning of that thing, for the
participants. E.g. in a stud y of the experiences of young people learning to
drive, we might find themes like ‘Driving as a rite of passage’ (where one munotes.in

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66 Qualitative Research key psychosocial understanding of the meaning of learning to drive, is that
it marks a cultural threshold between adolescence and ad ulthood).
Some themes will eventually be grouped under much broader themes
called ‘superordinate themes’. For example, ‘Feeling anxious and
overwhelmed during the first driving lessons’ might be a superordinate
category that captures a variety of patterns in participants’ embodied,
emotional and cognitive experiences of the early phases of learning to
drive, where we might expect to find sub -themes relating to, say, ‘Feeling
nervous,’ ‘Worrying about losing control,’ and ‘Struggling to manage the
complexiti es of the task.’ The final set of themes are typically summarised
and placed into a table or similar structure where evidence from the text is
given to back up the themes produced by a quote from the text.
4.4.2 Discourse analysis :
Discourse analysis is a research method for studying written or spoken
language in relation to its social context. It aims to understand how
language is used in real life situations.
When you do discourse analysis, you might focus on:
 The purposes and effects of different types o f language
 Cultural rules and conventions in communication
 How values, beliefs and assumptions are communicated
 How language use relates to its social, political and historical context
Discourse analysis is a common qualitative research method in many
huma nities and social science disciplines, including linguistics, sociology,
anthropology, psychology and cultural studies. It is also called critical
discourse analysis.
Conducting discourse analysis means examining how language functions
and how meaning is c reated in different social contexts. It can be applied
to any instance of written or oral language, as well as non -verbal aspects
of communication such as tone and gestures.
Materials that are suitable for discourse analysis include:
 Books, newspapers and periodicals
 Marketing material, such as brochures and advertisements
 Business and government documents
 Websites, forums, social media posts and comments
 Interviews and conversations
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67 Research Methodology For Psychology By analyzing these types of discourse, researchers aim to gain an
underst anding of social groups and how they communicate.
Unlike linguistic approaches that focus only on the rules of language use,
discourse analysis emphasizes the contextual meaning of language.
It focuses on the social aspects of communication and the ways pe ople use
language to achieve specific effects (e.g. to build trust, to create doubt, to
evoke emotions, or to manage conflict).
Instead of focusing on smaller units of language, such as sounds, words or
phrases, discourse analysis is used to study larger c hunks of language,
such as entire conversations, texts, or collections of texts. The selected
sources can be analyzed on multiple levels.
Discourse analysis is a qualitative and interpretive method of analyzing
texts (in contrast to more systematic methods like content analysis). You
make interpretations based on both the details of the material itself and on
contextual knowledge.
4.5 NARRATIVE ANALYSIS; CONVERSATION ANALYSIS Researchers use narrative analysis to understand how research participants
constru ct story and narrative from their own personal experience. That
means there is a dual layer of interpretation in narrative analysis. First the
research participants interpret their own lives through narrative. Then the
researcher interprets the constructio n of that narrative.
Narratives can be derived from journals, letters, conversations,
autobiographies, transcripts of in -depth interviews, focus groups, or other
types of narrative qualitative research and then used in narrative research.
Examples of pers onal narratives :
Personal narratives come in a variety of forms and can all be used in
narrative research.
 Topical stories
 A restricted story about one specific moment in time with a plot,
characters, and setting, but doesn’t encompass the entirety of a
person’s life. Example: a research participant’s answer to a single
interview question
Personal narrative :
 Personal narratives come from a long interview or a series of long
narrative interviews that give an extended account of someone’s life.
Example: a r esearcher conducting an in -depth interview, or a series of
in-depth interviews with an individual over an extended period of
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68 Qualitative Research Entire life story :
 Constructed from a collection of interviews, observations, and
documents about a person’s life. Example: a historian putting together
the biography of someone’s life from past artifacts.
Capturing narrative data :
While humans naturally create narratives and stories when interpreting
their own lives, certain data collection methods are more conducive to
underst anding your research participants’ sense of self narrative. Semi -
structured interviews, for example, give the interviewee the space to go on
narrative tangents and fully convey their internal narratives. Heavily
structured interviews that follow a question answer format or written
surveys, are less likely to capture narrative data.
Transcribing narrative data :
As mentioned earlier, narrative analysis has dual layers of interpretation.
Researchers should not take narrative interviews at face value because
they are not just summarizing a research participant’s self -narrative.
Instead, researchers should actively interpret how the interviewee created
that self -narrative. Thus narrative analysis emphasizes taking verbatim
transcription of narrative interviews, where it is important to include
pauses, filler words, and stray utterances like “um….”.
For more information on transcription options, please see our guide on
how to transcribe interviews.
Coding in narrative analysis :
There are many methods for coding na rrative data. They range from
deductive coding where you start with a list of codes, and inductive coding
where you do not. You can also learn about many other ways to code in
our Essential Guide to Coding Qualitative Data or take our Free Course on
Qualit ative Data Analysis.
What is narrative research? :
In addition to narrative analysis, you can also practice narrative research,
which is a type of study that seeks to understand and encapsulate the
human experience by using in depth methods to explore the m eanings
associated to people’s lived experiences. You can utilize narrative research
design to learn about these concepts. Narrative analysis can be used in
narrative research as well as other approaches such as grounded theory,
action research, ethnology and more.
4.6 REFERENCES 1. Shaughnessy, J. J., Zechmeister, E. B. &Zechmeister, J. (2012).
Research methods in psychology . (9th ed..). NY: McGraw Hill. munotes.in

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69 Research Methodology For Psychology 2. Elmes, D. G. (2011). Research Methods in Psychology (9thed.).
Wadsworth Publishing.
3. Goodwin , J. (2009). Research in Psychology: Methods in Design
(6thed.). Wiley.
4. McBurney, D. H. (2009). Research methods . (8th Ed.). Wadsworth
Publishing.
5. Forrester, M. A. (2010). Doing Qualitative Research in Psychology: A
Practical Guide . Sage.

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