Page 39 - DLIS401_METHODOLOGY_OF_RESEARCH_AND_STATISTICAL_TECHNIQUES

Methodology of Research and Statistical Techniques

Notes in the sample) to more closely approximate the distribution of the parameter (the summary
description of that variable in the population). The standard error, inversely related to sample
size, indicates how closely a sample statistic approximates the population parameter. These
conditions are only met when samples are randomly selected out of a population, i.e., when
every element in the population has an equal chance of being selected in the sample.
A randomly selected sample of sufficiently large size (absolute size, not size proportionate to
the population) is assumed to be more representative for the population because the relevant
statistics will more closely approximate the parameters, or the findings in the sample are more
generalizable to the population. Representativeness of samples, or generalizability of sample
findings, both matters of degree, are the main advantages of probability sampling designs.
The accuracy of a sample statistic is described in terms of a level of confidence with which the
statistic falls within a specified interval from the parameter (the broader the interval, the
higher the confidence).

Did u know? The main disadvantage of probability sampling is the theoretical assumptions
(of infinity) never “really” apply.

(a) Simple Random Sampling
In simple random sampling, each element is randomly selected from the sampling frame.
Example: in an alphabetical list of all students enrolled at CU-Boulder, each student is given
a number ascending from 1, and 400 students are selected using a table of random numbers.
(b) Systematic Sampling
In systematic sampling, every kth element in a list is selected in the sample, the distance k
indicating the sampling interval. The systematic sample has a random start when the first
element is randomly chosen (out of numbers between 1 and k). Systematic sampling has the
advantage of being more practical but about as (sometimes more) efficient than simple random
sampling. A disadvantage is the danger of an arrangement of elements forming a pattern that
coincides with the sampling interval. Example: in a list of all students enrolled at CU-Boulder,
each 100th student, starting with the randomly chosen 205th, is selected. Later it turned out
that every other student in the list was female (and the entire sample female), since the
composer of the list though “perfect randomness” would lead to perfect probability samples.
(c) Stratified Sampling
Stratified sampling is a modification to the use of simple random and systematic sampling. It
is based on the principle that samples are more representative when the population out of
which they are selected is homogeneous. To ensure samples to be more representative, strata
of elements are created that are homogeneous with respect to the (stratification) variables
which are considered to correlate with other variables relevant for research (the standard error
for the stratification variable equals zero). Example (stratified & systematic): luckily we know
how stupid composers of student lists are, so we stratify students by sex (taking every other
student in our “perfectly randomized” list); we thus get two strata of students based on sex,
and select every 40th student in each stratum.
(d) Cluster Sampling
In cluster sampling, clusters of groups of elements are created, and out of each group, elements
are selected. This method is advantageous since often complete lists of the population are
unavailable. Cluster sampling is multi-stage when first clusters are selected, then clusters
within clusters (on the basis of simple random or systematic sampling, stratified or not), and

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