Chapter 6 The nature of quantitative research

Question 1

What is a research design and what factors influence the choice of research design ?

Answer 1

A research design provides a framework for the collection and analysis of data.
A choice of research design reflects decisions about the priority being given to a range of dimensions
of the research process. These include the importance attached to:
*expressing causal connections between variables;
*generalizing to larger groups of individuals than those actually forming part of the investigation
*understanding behaviour and the meaning of that behaviour in its specific social context;
*having a temporal (i.e. over time) appreciation of social phenomena and their interconnections.

Question 2

What are the 5 different research designs?

Answer 2

1. Experimental design
2. Cross-sectional design
3. Longitudinal design
4. Case-study design
5. Comparative design

Question 3

What are the main steps in quantitative research?

Answer 3

1. Elaborate theory (theoretical problem, research model, research question)
2. Device hypothesis
3. Select research design (experiment, cross-sectional, longitudinal etc.)
4. Device measures of concepts (operationalization)
5. Select research site(s)
6. Select research subjects/respondents (sampling)
7. Administer research instruments/ collect data
8. Process data (coding)
9. Analyze data (descriptive and inferential statistics)
10. Develop findings/ conclusions
11. Write up findings/ conclusions

Question 4

How do you use the hypothetical-deductive measure (hypothesis-testing)?

Answer 4

Use deduction to derive test implications from a theory
-> “If the theory is true then we should be able to observe this”

*Draw a randomized sample from the population that the theory claims to explain/predict and examine the distribution of variables in the sample
*Statistically examine the probability that the observed variable distributions would occur as a result of chance (that the null hypothesis is true and that there is no relationship between A and B)
*If this probability is less than 0,05 we feel confident that we can reject the null hypothesis and find support for the experimental hypothesis

Example
*Experimental hypothesis: A is related to B
*Null hypothesis: A is not related to B

Question 5

What are Hypotheses?

Answer 5

Hypotheses are carefully worded statements about the theoretically predicted causal relationship
between variables

Question 6

What are the 3 basic quantitative research designs?

Answer 6

1. Experiment with hypothesis-testing (within subject, between subject)
2. Hypothesis-testing study based on secondary data/databases (cross-sectional or longitudinal)
3. Hypothesis-testing study based on self-completion survey (cross-sectional or longitudinal)

Question 7

The experimental design involves a number standardized steps. Briefly outline these steps.

Answer 7

Experimental design and hypothesis-testing (between subject design) Procedure:

1. Random assignment of individuals to a experimental and a control group
2. Pretest of both groups (measure the dependent variable)
3. The experimental group receives the experimental treatment (manipulation of the independent
   variable) while the control groups does not
4. Posttest of both groups (measure the dependent variable)
5. The difference between the experimental and control group’s score on the dependent variable is computed to see if the experimental treatment has made a significant difference

Question 8

What are the pros and cons of Experimental design?

Answer 8

*Access to data given that you find volunteers (other students?)
*Classical scientific design that allows for the study of causal effects in isolation
*Challenging to create design and control experimental and control conditions
*Takes time and resources
*Primarily applicable to research questions related to magnitude and significance of causal effect

Question 9

Briefly outline the procedure of a hypothesis-testing study based on secondary data/databases.

Answer 9

1. Select sample (e.g., firms in a certain industry)
2. Download variables from data-base (e.g., Orbis, etc.)
3. Examine data based descriptive statistics and create variables
4. Perform statistical tests on hypotheses

Question 10

What are the pros and cons of a hypothesis-testing study based on secondary data?

Answer 10

*Fast and easy to access data
*Large range of variables in databases
*Limited by pre-specified variables in database
*Construct validity may be a problem
*Hard to establish causality

Question 11

Briefly outline the procedure of a hypothesis-testing study based on self-completion survey.

Answer 11

1. Select sample
2. Design survey
3. Send out survey
4. Receive survey
5. Check non-response
6. Code received survey
7. Examine data based on descriptive statistics and create variables
8. Perform statistical tests based on hypotheses

Question 12

What are the pros and cons of a hypothesis-testing study based on self-completion survey?

Answer 12

*You can design your own variables/measures
*Limited time to send out survey
*Measurement is hard which may harm validity/reliability
*Response rate often low
*Hard to establish causality
*Low level of control

Question 13

What is the goal of measurement?

Answer 13

The goal of measurement is to discover fine differences between individuals based on consistent (over time) measures that allows for estimating the degree of relationship between concepts

Question 14

What is a concept?

Answer 14

*Concepts are categories for the organization of ideas and observations.
*They are the building blocks of theory and represent the points around which business research is conducted.
*Concepts may provide an explanation of a certain aspect of the social world, or they may stand for things we want to explain.
*A concept like organizational performance may be used in either capaity ( e.g. as an explanation of culture) or as something to be explained (what are the causes of variation in organizational performance?)

Question 15

To what extent do the main steps in quantitative research follow a strict sequence?

Answer 15

*The sequence of stages is a kind of ideal-typical account that is probably rarely found in this pure form
*Research is rarely as linear and as straight-forward as the figure implies, but its aim is to do no more than capture the main steps and to provide a rough indication of their interconnections
*The model should be thought of as a general tendency rather than as a definitive description of all quantitative research.
*The book is essentially providing an account of good practice but failure to follow this associated with matters of time, cost, and feasibility

3 reasons why the gap between the ideal type and actual research practice can arise.
1. Reverse operationism: The process of quantitative research implies that concepts are specified and measures are then provided for them. This means that indicators must be devised which is the basis of the idea of ‘operationism’. It implies a deductive view of how research should proceed.
2. Reliability and validity testing:Most of the time, researchers do not follow some of the recommended practices. This is not to say that this research is necessarily unstable and invalid, but that we simply do not know.
3. Sampling: Sometimes the use of non-probability samples will be due to impossibility or extreme difficulty of obtaining probability samples. Yet another reason is that the time and cost involved in securing a probability sample are too great relative to the level of resources available. And yet a third reason is that sometimes the opportunity to study a certain group presents itself and represents too good an opportunity to miss.

Question 16

Do the steps in in quantitative research suggest a deductive or inductive approach to the relationship between theory and research?

Answer 16

Deductive: An approach to the relationship between theory and research in which research is conducted based on hypotheses and ideas derived from theory. The fact that we start off with theory signifies a broadly deductive approach to the relationship business researcher collects data. The next step is to devise the hypothesis and first then it is followed by steps addressing research.

(NOT) Inductive: An approach to the relationship between theory and research in which theory emerges from research.

Question 17

Why is measurement important for the quantitative researcher?

Answer 17

There are three main reasons:
1. Allows them to distinguish differences between individuals.
2. Provides a consistent method for making those distinctions.
3. Enables more precise estimates of the relationship between concepts.

Question 18

What is the difference between a measure and an indicator?

Answer 18

Measure: Is taken to refer to things that can be relatively unambiguously counted. Measures, in other words, are quantities. We use indicators to tap concepts that are less directly quantifiable. An indicator, is something that is devised or already exists and that is employed as though it were a measure of a concept (often called operational definition) .

Indicator: A measure that is employed to refer to a concept when no direct measure is available.

Question 19

Why might multiple-indicator approaches to the measurement of concepts be preferable to those that rely on a single indicator?

Answer 19

• Possible that a single indicator will incorrectly classify many individuals. This may be due to the wording of the question or it may be a product of misunderstanding.
• One indicator may capture only a portion of the underlying concept or be too general. A single question may need to be of an excessively high level of generality and so may not reflect the true state of affairs for the people replying to it. Alternatively, a question may cover only one aspect of the concept in question.
• You can make much finer distinctions.
  Possibility that the concept in which you are interested comprises different dimensions- people scoring high on one dimension may not necessarily score high on other dimensions, so that for each respondent you end up with a multidimensional ‘profile’.

Question 20

What are the main ways of thinking about the reliability of the measurement process? Is one form of reliability the most important?

Answer 20

• Reliability refers to the consistency of a measure of a concept. The following are three prominent factors involved when considering whether a measure is reliable:
  1. Stability. This consideration entails asking whether or not a measure is stable over time, so that we can be confident that the results relating to that measure for a sample of respondents do not fluctuate.
  2. Internal reliability!!. The key issue is whether or not the indicators that make up the scale or index are consistent—in other words, whether or not respondents’ scores on any one indicator tend to be related to their scores on the other indicators (e.g., Cronbach’s alpha).
  3. Inter-observer consistency. When a great deal of subjective judgment is involved in such activities as the recording of observations or the translation of data into categories and where more than one ‘observer’ is involved in such activities, there is the possibility that there is a lack of consistency in their decisions.

Question 21

‘Whereas validity presupposes reliability, reliability does not presuppose validity.’ Discuss.

Answer 21

If a measure of a concept is unstable in that it fluctuates and hence is unreliable, it simply cannot be providing a valid measure of the concept in question. In other words, the assessment of measurement validity presupposes that a measure is reliable.

For example, if a bathroom scale consistently shows the same weight every time someone steps on it, it is reliable. However, if the scale consistently shows a weight that is different from the person’s true weight, then it is not valid. Thus, reliability is a necessary but not sufficient condition for validity.

Question 22

What are the main criteria for evaluating measurement validity?

Answer 22

Validity: The extent to which a coded variable measures the intended theoretical concept. There are a number of types of validity.

• Face validity: The measure appears to measure what it claims to measure based on its face value and intuition.
• Concurrent validity: The measure is consistent with other measures that are accepted as valid at the same time.
• Predictive validity: The measure is consistent with expected future outcomes or behaviors.
• Construct validity: The measure functions as expected in relation to other variables or constructs in a causal model or theoretical framework.
• Convergent validity: The measure is similar to and correlates well with other measures developed through different methods that measure the same or similar constructs.

Question 23

Outline the main preoccupations of quantitative research. What reasons can you give for their prominence?

Answer 23

1. Measurement (validity and reliability should be high)
2. Causality (explaining should be reduced to identifying cause and effect relationships)
3. Generalization (the results should apply to as many individuals as possible)
4. Replication (other researchers should be able to do the study over again and reach the same results)

Question 24

Why might replication be an important preoccupation among quantitative researchers, in spite of the tendency for replications in business research to be fairly rare?

Answer 24

Replication is important for quantitative researchers because it helps to establish the validity of their findings and the reliability of their methods. In other words, if other researchers can replicate the same results using the same methods, it increases confidence that the original findings were not due to chance or bias. However, replications in business research are often rare, possibly due to practical constraints or concerns about proprietary data.

Question 25

What are the four main criticisms of quantitative research?

Answer 25

Quantitative research has four main criticisms:

1. It treats people and social institutions as if they were part of the natural world.
2. The measurement process can create a false sense of precision and accuracy.
3. The use of instruments and procedures can create a gap between research and everyday life.
4. Analyzing relationships between variables can create a static view of social life that doesn’t consider people’s experiences.