Page 38 - DLIS401_METHODOLOGY_OF_RESEARCH_AND_STATISTICAL_TECHNIQUES

Unit 2: Research Design

Rule 2: Time-order Notes

When Y appears after X, Y cannot have caused X, or in other words, the cause must have
preceded the effect in time. Derivative from this is the rule that when X is relatively stable,
hard to change, and fertile (it produces many other effects), it is likely to be the independent
variable.

Rule 3: Non-Spuriousness
When the observed correlation between two variables is the result of a third variable that
influences both of those two separately, then the correlation between the two is spurious. This
is indicated by a variable having a causal path to the two variables that correlate.
Basic to causality is the control of variables. Most ideally, this is done by randomization in
experiments, then the attributes of any prior variables are randomly distributed over the
control and the experimental group. We can also purposely control for prior variables when
we select the ones we consider relevant. In bivariate relationships, no variables are controlled,
while in partial relationships, one or more of the prior and intervening variables, that might
interfere, are controlled. It is better still to identify the necessary and sufficient causes of
certain effects but usually we are pleased with either one.
Some common errors are—biased selection of variables to be included in the model, unwarranted
interpretation, suppression of evidence, and so on. It is interesting to see the different steps
involved in a typical causality-type research and what can go wrong at each step. First, from
theory to conceptualization, this step is rarely clear-cut. Second, the step into operationalization
is in a way always arbitrary (since the concept indicates more than any measurement). Third,
the empirical associations found between measured variables is rarely, if ever, perfect. Finally,
any measurement therefore requires additional studies, and any conclusion is in principle
falsifiable (variables are shown to be associated, but then the question is how they are associated).
Strategies for causal analysis—When a bivariate non-zero relationship between X and Y is reduced
to zero under control of a third variable, then the third variable explains the bivariate relationship,
or the relationship is spurious (causality can never be proven by data analysis); Check out for
the effect of prior variables; Path analysis.

2.1.4 Sampling Procedures
Sampling refers to the systematic selection of a limited number of elements (persons, objects
or events) out of a theoretically specified population of elements, from which information will
be collected. This selection is systematic so that bias can be avoided. Observations are made
on observation units, which can be elements (individuals) or aggregations of elements (families).
A population is theoretically constructed and is often not directly accessible for research.
Therefore, the study population, the set of elements from which the sample is actually selected,
can (insignificantly) differ from the population. In multi-stage samples, the sampling units
refer to elements or sets of elements considered for selection at a sampling stage. The sampling
frame is the actual list of sampling units from which the samples are selected.
The sampling procedures are designed to best suit the collection of data, i.e., to measure the
attributes of the observation units with regard to certain variables. Depending on theoretical
concerns and choice of method, probability or non-probability sampling designs are appropriate
in research.
1. Probability Sampling
Probability sampling is based on principles of probability theory which state that increasing
the sample size will lead the distribution of a statistic (the summary description of a variable

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