researchers to achieve the appropriate data. Selecting a
correct sampling technique depends on the population,
availability, and accessibility of the resources (Saunders
et al., 2009). Probability sampling (representative) and
non-probability sampling (judgmental) are two kinds of
sampling techniques. A probability sampling guarantees that
the entire population has an equal chance to be selected.
Simple random, systematic, cluster, stratified random, and
multi-stage are five techniques under probability sampling
(Saunders et al., 2009, 2016). Non-Probability sampling is
used when the entire population cannot be selected. Purposive, quota, self-selection, snowball, and convenience are the
techniques under non-probability sampling (Saunders et al.,
2009).
6.4.1 Target Population
The process starts with defining the target population and
specifying the sampling frame. The target population is the
main population of the research study, and it is a subset of
the overall population (Saunders et al., 2016). The target
population for this research study was the entire teaching
staff of Saudi Higher Educational Institutions. However, this
study is limited to the institutions of the Eastern Province of
Saudi Arabia where the medium of instruction is English.
The institutions include six universities and two community
colleges (see the following Table 6.2). All emails sent
through ITC-KFUPM reach all departments, including all
community colleges under its umbrella. There are seven
technical colleges, one military college, and one women’s
medical college in the Eastern region, but were not incorporated in the data collection because the medium of
instruction in most of these colleges is Arabic. Furthermore,
it was not possible to approach the women’s colleges to get
the data. Regarding the total number of instructors in the
target academic institutions, the following information was
collected through visiting their websites and contacting with
the Human Resource department via phone and email.
6.4.2 Sampling Frame
A sampling frame is “the complete list of all the cases in the
population, from which a probability sample is drawn”
(Saunders et al., 2016, p.727). This study deals with the
users (adopters) of the LMS. In an exploratory study, it was
discovered that there are 58% adopters and 42%
non-adopters of the LMS. The following Fig. 6.2 adopted
from De Vaus (2002) shows moving from population to
sample size.
6.4.3 Sample Size for Quantitative Data
In this study, the approximate target population size is 2005.
A survey questionnaire was sent to the entire population of
HEIs of the Eastern Province of Saudi Arabia. To calculate
the sample size, the following procedures were adopted
(without using standard deviation) as given in the following
paragraphs:
Saunders et al. (2016), recommended the following steps
for calculation of sample size proposed by De Vaus (2014):
Step 1 : n ¼
z
2
 p
ð Þ Â 1 À p
ð
Þ
c 2
Step 2 : n
0
¼
n
1 þ n
N
n
Minimum sample size = ?
z
confidence level (e.g. at 95% confidence level) = 1.96
p
population proportion (the percentage belonging to the
specified category). From exploratory study, it was
found that 40 out of 69 instructors are the adopters of
the LMS. (see preliminary study) Therefore, the
proportion of the users p = (40/69 Â 100) = 58%
c
confidence interval (e.g. ± 5) = 5
N Target population = 2005
Table 6.1 Summary of research
design and methodology used for
this study
Layer
number
Layer name
Adopted approach
First-layer
Philosophy
Positivism
Second-layer
Research approach
Deductive
Third-layer
Research strategy
Quantitative
Fourth-layer
Methodology choice
Survey
Fifth-layer
Nature of research-time
horizon
Cross-sectional
Sixth-layer
Data collection and
analysis
–Techniques and procedures of quantitative analysis such
as EFA, CFA
Source Saunders et al. (2016)
6.4 Sampling Techniques
53
correct sampling technique depends on the population,
availability, and accessibility of the resources (Saunders
et al., 2009). Probability sampling (representative) and
non-probability sampling (judgmental) are two kinds of
sampling techniques. A probability sampling guarantees that
the entire population has an equal chance to be selected.
Simple random, systematic, cluster, stratified random, and
multi-stage are five techniques under probability sampling
(Saunders et al., 2009, 2016). Non-Probability sampling is
used when the entire population cannot be selected. Purposive, quota, self-selection, snowball, and convenience are the
techniques under non-probability sampling (Saunders et al.,
2009).
6.4.1 Target Population
The process starts with defining the target population and
specifying the sampling frame. The target population is the
main population of the research study, and it is a subset of
the overall population (Saunders et al., 2016). The target
population for this research study was the entire teaching
staff of Saudi Higher Educational Institutions. However, this
study is limited to the institutions of the Eastern Province of
Saudi Arabia where the medium of instruction is English.
The institutions include six universities and two community
colleges (see the following Table 6.2). All emails sent
through ITC-KFUPM reach all departments, including all
community colleges under its umbrella. There are seven
technical colleges, one military college, and one women’s
medical college in the Eastern region, but were not incorporated in the data collection because the medium of
instruction in most of these colleges is Arabic. Furthermore,
it was not possible to approach the women’s colleges to get
the data. Regarding the total number of instructors in the
target academic institutions, the following information was
collected through visiting their websites and contacting with
the Human Resource department via phone and email.
6.4.2 Sampling Frame
A sampling frame is “the complete list of all the cases in the
population, from which a probability sample is drawn”
(Saunders et al., 2016, p.727). This study deals with the
users (adopters) of the LMS. In an exploratory study, it was
discovered that there are 58% adopters and 42%
non-adopters of the LMS. The following Fig. 6.2 adopted
from De Vaus (2002) shows moving from population to
sample size.
6.4.3 Sample Size for Quantitative Data
In this study, the approximate target population size is 2005.
A survey questionnaire was sent to the entire population of
HEIs of the Eastern Province of Saudi Arabia. To calculate
the sample size, the following procedures were adopted
(without using standard deviation) as given in the following
paragraphs:
Saunders et al. (2016), recommended the following steps
for calculation of sample size proposed by De Vaus (2014):
Step 1 : n ¼
z
2
 p
ð Þ Â 1 À p
ð
Þ
c 2
Step 2 : n
0
¼
n
1 þ n
N
n
Minimum sample size = ?
z
confidence level (e.g. at 95% confidence level) = 1.96
p
population proportion (the percentage belonging to the
specified category). From exploratory study, it was
found that 40 out of 69 instructors are the adopters of
the LMS. (see preliminary study) Therefore, the
proportion of the users p = (40/69 Â 100) = 58%
c
confidence interval (e.g. ± 5) = 5
N Target population = 2005
Table 6.1 Summary of research
design and methodology used for
this study
Layer
number
Layer name
Adopted approach
First-layer
Philosophy
Positivism
Second-layer
Research approach
Deductive
Third-layer
Research strategy
Quantitative
Fourth-layer
Methodology choice
Survey
Fifth-layer
Nature of research-time
horizon
Cross-sectional
Sixth-layer
Data collection and
analysis
–Techniques and procedures of quantitative analysis such
as EFA, CFA
Source Saunders et al. (2016)
6.4 Sampling Techniques
53