Page 42 - DLIS401_METHODOLOGY_OF_RESEARCH_AND_STATISTICAL_TECHNIQUES

Unit 2: Research Design

(how) as well as the way in which the data are to be analyzed, and what the anticipated Notes
findings are.
• Finally, after the research is conducted, a report is drawn up, indicating theory, methodology,
as well as findings.
A. Experimental Designs
The most important issue in an experiment is randomization (as a matter of internal validity).
There are issues of internal and external validity, and the problems and solutions of external
validity. Note the strength and limitations with regard to the control of variables, i.e., all the
variables we know might interfere.
1. The Structure of Experiments

A classical experiment involves four basic components:
1. An experiment examines the effect of an independent variable on a dependent variable.
Typically, a stimulus is either absent or present. In this way, a hypothesis on the causal
influence between two variables can be tested. Both variables are, of course, operationalized.
2. An experiment involves pretesting and posttesting, i.e., the attributes of a dependent
variable are measured, first before manipulation of the independent variable, and second
after the manipulation. Of course, applied to one group, this may affect the validity of
the results, since the group is aware of what is being measured (research affects what is
being researched).

3. Therefore, it is better to work with experimental groups and control groups. We select
two groups for study, then apply the pretesting-posttesting, and thus conclude that any
effect of the tests themselves must occur in both groups. There can indeed be a Hawthorne
effect, i.e., the attention given to the group by the researchers affects the group’s behavior.
Note that there can also be an experimenter bias, which calls for accurate observation
techniques of the expected change in the dependent variable.
4. Selecting Subjects—there can always be some bias because often students are selected
(problem of generalizability). Also, note that samples of 100 or not very representative,
and that experiments often have fewer than 100 subjects.
Randomization refers to the fact that the subjects (which are often non-randomly selected from
a population) should be randomly assigned to either the experimental or the control group.
This does not ensure that the subjects are representative of the wider population from which
they were drawn (which they usually are not), but it does ensure that the experimental and
the control group are alike, i.e., the variables that might interfere with the results of the
experiment will, based on the logic of probability, be equally distributed over the two groups.

Notes Randomization is related to random-sampling only in the sense that it is based on
principles of probability (the two groups together are a “population”, and the split
into two separate groups is a random-sampling into two samples that mirror eachother
and together constitute this “population”).

Matching refers to the fact that subjects are purposely assigned by the researcher to either the
control or the experimental group on the basis of knowledge of the variables that might
interfere with the experiments. This is based on the same logic as quota sampling. Matching
has the disadvantage that the relevant variables for matching decisions are often not all known,
and that data analysis techniques assume randomness (therefore, randomization is better).

LOVELY PROFESSIONAL UNIVERSITY 37