Chapter 2
METHOD
This chapter explains and describes the research design, research
respondents, research locale, research instrument, data collection and
statistical tools.
Research Design
A research design is the specific method of a researcher that sets the
procedure for collecting, analyzing, interpreting and reporting data in research
studies (Creswell, 2012). It is the overall plan for connecting the conceptual
research problems with the pertinent (and achievable) empirical research. In
other words, the research design sets the procedure on the required data, the
methods to be applied to collect and analyze this data, and how all of this is
going to answer the research question (Anderson, 2012). This study will employ
descriptive-correlational design. Descriptive design provides a snapshot of the
current state of affairs. This design according to Arikunto (2007) is intended to
gather some information regarding the trend found in the field. It means that
there is no administration and control in this kind of research. He further justified
that investigating the correlation between variables is classified in the form of
correlation coefficient.
The viewpoints of Arikunto (2007) is allied with the approach of Creswell
(2012) that in correlational research design, investigators use the correlation
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statistical test to describe and measure the degree of association/relationship
between two or more variables or sets of scores among variables and to allow
the prediction of future events from present knowledge. Hence, significant
justification from renowned authors shed light to the researcher that the
combination of descriptive and correlational design is the most appropriate
composition to address research questions regarding the level and relationship
of online teaching readiness and sex on the technology integration knowledge
of teachers.
Further, the study will utilize regression analysis to answer research
question number 5 found in the preceding chapter. As claimed by Casella &
Berger (2002) creating a model using regression analysis is a powerful and
flexible framework that allows an analyst to model an outcome (the response
variable) as a function of one or more explanatory variables (or predictors). This
regression analysis can help researchers understand how values of a
quantitative (numerical) outcome (or response) are associated with values of a
quantitative explanatory (or predictor) variable. This technique is often applied
in two ways: to generate predicted values or to make inferences regarding
associations in the dataset. In some disciplines the outcome is called the
dependent variable and the predictor is the independent variable.
Research Respondents
In establishing inclusion criteria for study participants/respondents is a
standard, required practice when designing high-quality research protocols.
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Inclusion criteria are defined as the key features of the target population that the
researchers/investigators will use to answer their research question (Montes de
Oca et al., 2017). More importantly, researchers/investigators not only define
the appropriate inclusion criteria when designing a study but also evaluate how
those decisions will impact the external validity of the results of the study (Hulley
et al., 2007). On this note, male and female teachers will be included in the
study regardless of their age, religion, beliefs, employment status, salary and
wages, field of expertise, and department they belong.
In determining the sample size, the article of Fernandez et al. (2009)
justified that sample size is one element of research design that investigators
need to consider as they plan their study. Reasons to accurately calculate the
required sample size include achieving statistically significant result and
ensuring research resources are used efficiently and ethically. Similar principles
apply when considering an adequate sample size for regression analyses.
Regression analysis is used to estimate a relationship between predictors
(independent variables) and a continuous dependent variable. Sample size for
this type of analysis can use the 20:1 rule which states that the ratio of the
sample size to the number of parameters in a regression model should be at
least 20 to 1. This justification will be used in appropriating number of
respondents that will be included in the study. The current research project
consists of three parameters (two independent variables and one dependent
variable). Hence, taking into consideration in applying 20:1 rule, the number
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samples will result to 60 respondents to answer the adapted survey
questionnaires.
Prior to examining of type of sampling method to apply in the study, it is
worth noting what is meant by sampling, along with reasons why researchers
are likely to select a sample. Taking a subset from chosen sampling frame or
entire population is called sampling. Sampling can be used to make inference
about a population or to make generalization in relation to existing theory. In
essence, this depends on choice of sampling technique (Hosseini & Kamal,
2012). It is in this context, that the researcher resorts to use simple random
sampling which means that every case of the population has an equal
probability of inclusion in sample.
Research Locale
The study will be conducted to one of the higher learning institutions in
Davao City offering Senior High School Program and Baccalaureate Degree
Programs. This academic institution exists for about 20 years now capacitating
the potentials, learning ability and intellectual prowess of students. Shown in
Figure 2 is the Map of the republic of the Philippines showing the location of
Davao City.
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Figure 2. Map of the Republic of the Philippines
Showing Davao City
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Research Instrument
There will be two sets of survey questionnaire to be used to gather data
from the respondents of the study. The first set of the questionnaire is the online
teaching readiness developed by Martin & Chuang (2018). The second of the
instrument is the technology integration knowledge designed by Hosseini &
Kamal (2012). To meet the requirements of validity and reliability of the research
instrument, the researcher will honor the fidelity of undergoing the research tool
from face validity to reliability test. It is accentuated by Field (2005) that validity
explained how well the collected data covers the actual area of investigation. In
this account, the survey questionnaire will be forwarded to panel of experts in
questionnaire construction for modification process to fit in the culture of the
respondents.
In the context of reliability, the research instrument will undergo the
process of pilot testing because reliability is concerned with the extent to which
a measurement of a phenomenon provides stable and consistent result. It is
acclaimed by De Leeuw (2010) that testing for reliability is important as it refers
to the consistency across the parts of a measuring instrument. Consequently,
Huck (2014) accentuated that a scale is said to have high internal consistency
and reliability if the items of a scale “hang together” and measure the same
construct. He further explained that the most commonly used internal
consistency measure is the Cronbach Alpha coefficient. It is viewed as the most
appropriate measure of reliability when making use of Likert scales. From the
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point of view of Whitley (2010), there is no absolute rules exist for internal
consistencies, however most agree on a minimum internal consistency
coefficient of 0.70.
In this study, Five-point Likert’s scale will be used for it is one of the most
commonly used scales. To evaluate the level of online teaching readiness, the
following scales will be employed:
Range of Means Descriptive Equivalent Interpretation
When the online teaching
4.20-5.00 Very High readiness of teachers is always
manifested.
When the online teaching
3.40-4.19 High readiness of teachers is
oftentimes manifested.
When the online teaching
2.60-3.39 Moderate readiness of teachers is
sometimes manifested.
When the online teaching
1.80-2.59 Low readiness of teachers is rarely
manifested.
When the online teaching
1.00-1.79 Very Low readiness of teachers is not
manifested at all.
To assess the level of technology integration knowledge, the following
scales will be utilized:
Range of Means Descriptive Equivalent Interpretation
When the technology integration
4.20-5.00 Very High knowledge of teachers is always
manifested.
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When the technology integration
3.40-4.19 High knowledge of teachers is
oftentimes manifested.
When the technology integration
2.60-3.39 Moderate knowledge of teachers is
sometimes manifested.
When the technology integration
1.80-2.59 Low knowledge of teachers is rarely
manifested.
When the technology integration
1.00-1.79 Very Low knowledge of teachers is not
manifested at all.
Data Collection
At the outset, the researcher set appointments to his adviser for
consultation in the conceptualization of the research framework. Upon approval
of the adapted survey questionnaire is organized and will be submitted to panel
of examiners for face validation purposes. Likewise, this research tool will be
administered to teachers to obtain the value of Cronbach’s Alpha. In addition,
the researcher will ask permission from the Executive Vice President to conduct
the present study to teachers across academic departments. More so, the
researcher will personally distribute the tool to teachers and explained to them
the rationale behind the research problems. Hereafter, the researcher will
retrieve the survey questionnaire after the respondents answered all the items
stipulated in the tool. Tabulation of the data will be done for statistical treatment.
Henceforward, statistical results will be analyzed meticulously and interpreted
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with professional prudence to establish meaningful findings, conclusions and
recommendations.
Statistical Tools
To address the fundamental objective of the study, the following
statistical tools will be used for data treatment:
Mean and Standard Deviation – This will be used to determine the level
of online teaching readiness and technology integration knowledge of teachers.
Pearson (r) – This will be used to determine the significant relationship
between online teaching readiness and age on the technology integration
knowledge of teachers.
Regression Analysis – This will be used to determine the singular and
combined influence of online teaching readiness and sex on the technology
integration knowledge of teachers. This will be utilized as basis in crafting
regression model.
Dummy Coding – This will be used to code the categorical predictor
variable for inclusion into the regression model.
