Chapter-3-22.pdf as the sample of paper witing

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III. METHODS
Introductory Paragraph
This chapter presents the research methodology for this study. It describes the research
design, the research variables, the population, and the sampling, the research tools, the pilot
study, the data analysis, the study procedures, the research procedures, the data collection,
and the research conclusion.
3.1 Target Population and Sampling Strategies
This study is conducted at one of the best private secondary schools located in Phnom Penh
City, Cambodia. As indicated by Israel (1992) and Singh & Masuku (2014), to achieve an
ideal degree of accuracy, almost the whole population would need to be sampled in little
groups. At the point when the population is 500, the sample size is 100 depending on ±10%
of the useful degree of exactness. Many researchers add 10% to the sample size to
accommodate for those who are unable to be contacted, as sample size determination
processes suggest the number of responses that must be gathered (Israel, 1992; Singh &
Masuku, 2014). According to the preceding authors, the sample size is usually increased by
30% to account for no-response.
Table 3. 1
Sample Size Determination
Sample Size for ±5% and ±10% Precision Levels where Confidence Level is 95% and
P=0.5.
Size of Population
Sample Size (n) for precision (e)
500 222 83
1,000 286 91
2,000 333 95

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3,000 353 97
4,000 364 98
5,000 370 98
7,000 378 99
9,000 383 99
10,000 385 99
15,000 390 99
20,000 392 100
25,000 394 100
50,000 397 100
100,000 398 100
>100,000 400 100
Table 3. 1 Adopted from Israel (1992), Singh and Masuku (2014).
3.1.1.1 Research Instrument
The study used a questionnaire as a tool. The questionnaire is in both English and Khmer
versions. The students' administration of the questionnaire was considered most appropriate
for collecting data in this study. Data collection is carried out confidentially and privately.
The questionnaire was administered directly to the participants.
3.1.1.2 Students demographic profile
The researcher created the demographic profile of the respondents based on the collection
with information on educators such as genders and ages. The questions are designed

105
speaking with the questionnaires that included in both sections A and B about teaching
speaking.
3.1.1.3 Student motivation instrument
The items for motivation are involved using five-point Likert-type scales. Items on the
scales are verified at 1 = Strongly disagree, 2 = Slightly disagree, 3 = Disagree, 4 = Slightly
agree and 5= Strongly agree. The entire review was recorded in the reference area (Tuan,
2011; Chen & Shieh, 2005).
3.1.1.4 Student performance instrument
The scale is used to evaluate the peer and self-assessment for the following introduction
chart below please carefully rate how students felt about peer and self-assessment. Use the
scale of 1 to 5 with 1. Strongly disagree 2. Slightly disagree 3. Disagree 4. Slightly agree
5. Strongly agree. Circle the most appropriate one (Lee & Chang, 2005) (Appendix D).
3.1.1.5 Teaching speaking instrument
The scale is used to evaluate the teaching speaking for the following introduction chart.
Please carefully rate how students felt about role play. The outcome space for Likert scales
is made up of a limited range of possible responses on ranges, such as disagree or agree.
Most Likert scales should be made from one to five points. For scales with more than four
or five categories (Smith et al., 2003) for a detailed study concerning this issue. One to five
points is desirable for student respondents and respondents with low motivation to
complete the questionnaire because the 1-to-5-point scales are easy to understand and
require effort to answer. When possible, however, 1-to-5-point scales should be used as
they permit the possibility of increased measurement precision. Use the scale of 1 to 5 with
1. I strongly disagree. 2. I slightly disagree. 3. Contrast4. I agree in part.5. agree
wholeheartedly (Nemoto & Beglar, 2014).
3.1.1 Ethical Issues

106
Ethical issue is an important part of the data collection process. There are three important
ethical concerns to be met by the researcher: informed agreement, dishonesty, and
confidentiality. First of all, to ensure the anonymity and protect the personal data of the
participants, the information letter was sent to the school principals for approval before the
start of the research. This letter included the students’ questionnaire form. Second, all the
information about the plan was given earlier to the informants, and the same information
was repeated before students filled out questionnaires. Informers were told that neither their
names nor the name of the school would be mentioned in the research. The informers were
asked if it was fine with them for the conversation to be recorded, and they signed the
information sheet before the recording started. The participants were also aware of the
purpose of the research, the procedures used during the research, the risks and benefits of
the research, the voluntary nature of the research participants, their right to stop the
research at any time, and the procedures used to protect confidentiality. Third,
confidentiality is a very significant issue, so it should be pointed out that no names or
places were mentioned during the recording session or written on the questionnaire forms.
The sessions were noted on the personal recorder of the researcher, were not disclosed to
anyone, and were deleted shortly after the survey copy. While it could be argued that these
enthusiastic systems are proper to defend members' feelings, we feel that an over emphasis
on box-ticking for scientists may, on occasion, be to the disadvantage of their commitment
to more profound moral issues. This undeniably regulatory methodology could prompt
unexperienced insider scientists to keep away from, or not connect completely with, what
has been named the ethic of care, since we may feel that by acquiring moral endorsement
toward the beginning of their undertaking, they don't have to worry about such issues any
further. However, it isn't adequate to expect that an understanding of educated assent that
comes toward the beginning of the exploration relationship covers the entire investigation.
An illustration of this is the idea of anonymity: What we are attempting to clarify is that
secrecy is a worry all throughout the request. As specialists, we should know that the scene
and the people with whom we are drawing in as members might be moving and involved.
Ethical responsibilities are not generally time-restricted. However, in most examinations,
the idea of an untouchable inclusion implies that once the exploration has been finished and
reviewed, moral concerns are normally out of the spotlight. This isn't a situation for

107
insiders, especially in light of the longitudinal examination. Current practice in associations
regularly relies upon the verifiable point of reference, so even after time has slipped by,
sensitivities exist about the chronicled record, not least in regards to who might be
reprimanded for what, while managing a portion of the private matters that had emerged at
the time, which suggests that insider specialists need to keep up moral responsibilities into
the drawn-out future. This model leads us to the idea of inner moral commitment, to which
we currently turn. As previously defined, inner moral commitment refers to the moral and
ethical issues that insider analysts must deal with while on the job, which are related to
ongoing individual and professional relationships with members, insider information,
competing professional and scientific jobs, and namelessness. In this segment, we will
investigate every one of these issues, drawing on our encounters as insider specialists.
3.2 Research Techniques
This study applied the Quantitative method because of the research gap in chapter two
which suggested this topic.
Quantitative examination and techniques explicitly depend on mathematical information
that can be changed into usable statistics. Quantitative techniques are regularly identified
with the positive worldview where the positive accepts that lone a solitary quantifiable
reality exists (Omarsson, 2017).
The questionnaire included the factors as independent variables measured on five-point
Likert scale items developed for this study and the number of items is indicated in supports:
time-planning skills (1); decreased time for communication with colleagues (2); possibility
to work from home (3); supervisor's trust (4); supervisor's support (5); possibility to reduce
travel expenses (6); possibility to care for family members (7); suitability of the working
place (8). As independent variables, and we also measured gender, and the number of
children. Two five-point Likert scale items were used to assess the dependent variable of
subjective career opportunities. The other three dependent variables, overall satisfaction
with the work, perceived benefits of work, and self-reported productivity, were assessed
using a single five-point Likert scale items. All of these items are shown, and described
(Nakrošienė et al., 2019).

108
Creswell (2014) Book Research Design: Quantitative Method Approach discusses the
approach: Quantitative method. This educational book is both informative and illustrative,
and it will benefit to students, teachers, and researchers alike. For a better understanding of
this book, readers should have a basic understanding of research. The book is divided into
two sections. Part I describes the steps for developing a research proposal, while Part II
describes how to develop a research proposal or write a research report. At the end of each
chapter, a summary is provided to assist the reader in reviewing the ideas. Furthermore, the
writing exercises and suggested readings at the end of each chapter are beneficial to the
readers. Chapter 1 begins with a definition of research approaches, and the author expresses
the belief that the choice of a research approach is influenced by the nature of the research
problem, the experience of the researchers, and the study's audience. The author
differentiates between qualitative, quantitative, and mixed methods research. There is a
difference between quantitative and qualitative research approaches. According to the
author, interest in qualitative research grew in the final half of the twentieth century. The
worldviews, or paradigms, as Fraenkel, Wallen, and Hyun (2012) and Onwuegbuzie and
Leech (2005) refer to them, have been explained. The use of language can become overly
philosophical and technical at times. This is most likely due to the author's need to explain
some technical terms (Ishtiaq, 2019).
Because the quantitative paradigm is founded on objectivism and positivism, it is referred
to as scientific research (Creswell, 2014; Ma, 2012; Jonker & Pennink, 2010). The
quantitative standard holds that there is only one objective reality that is distinct from the
researcher's perceptions. The researcher is unaffected by the phenomenon under
investigation; neither affected nor affected by it. The primary goal of quantitative research
is to quantify causal relationships through the use of a value-free framework (Johnson &
Onwuegbuzie, 2004; Sale et al., 2002). The quantitative approach is based on the collection
and analysis of quantitative data and observes to the quantitative research paradigm
(Bryman & Bell, 2007; Johnson & Christensen, 2012). It is a confirmatory or deductive
approach, with the primary goal of testing theories and hypotheses by examining the
relationships between variables (Antwi & Hamza, 2015; Bryman & Bell, 2007; Johnson &
Christensen, 2012; Creswell, 2014). The qualitative approach, on the other hand, adheres to

109
the qualitative research paradigm, and it is based on the collection and analysis of
qualitative data (Bryman & Bell, 2007; Johnson & Christensen, 2012). It is an exploratory
or inductive approach that seeks to investigate and comprehend the meanings that
individuals or groups assign to social phenomena (Bryman & Bell, 2007; Johnson &
Christensen, 2012; Creswell, 2014) according to (Maarouf, 2019).
Quantitative data was derived from the primary and secondary sources discussed earlier in
this chapter. This data analysis was performed using Excel, SPSS 20.0, Office Word
format, and other tools based on their data type. This data analysis is primarily concerned
with numerical or quantitative data analysis. Data coding of responses and analysis were
performed prior to the analysis. The data obtained from questionnaires were coded to SPSS
20.0 software in order to easily analyze the data obtained. This task entailed identifying,
classifying, and assigning a numeric or character symbol to data in only one pre-coded
way. All of the responses in this study were pre-coded. They were chosen from a list of
responses, and a number corresponding to each selection was assigned. This procedure was
followed for every previous question that required it. Following completion, the data was
transferred to a statistical analysis software package, SPSS version 20.0 on Windows 10,
for further processing. Data exploration has been carried out using descriptive statistics and
graphical analysis as part of the data analysis. The analysis included investigating the
relationship between variables and comparing how groups affect one another. This was
accomplished through the use of cross-tabulation, chi-square, correlation, and factor
analysis, as well as nonparametric statistics (Sileyew, 2019).
3.2.1.1 Research Variables
There are two variables in this research, namely independent variable and dependent
variables.
3.2.1.2 Independent variable
There is one independent variable: teaching speaking skills.

110
3.2.1.3 Dependent Variables
There are two dependent variables: student motivation and student performance
3.3.1 Pilot Study procedure and Pilot Study Cronbach Alpha
The pilot study was conducted with 30 students who were learning English subjects at a
private secondary school. The survey was introduced to any issue that could be mistaken by
respondents and as a result supposed poor response rates (Beach et al., 2005)
3.3.2 Pilot study procedures
Step 2: Give the questionnaires
Respondents of private secondary schools
Step 3: Answer the questionnaires
Respondents will be asked to complete the
questionnaires.
Step 1: Explain
Giving clear instruction to all respondents
Respondents are questioned to answer the questions related to
learning speaking
Step 4: Do the Peer-Assessment
Respondents ask each other in term of peer-
assessment. Offering the material in the class
with the clear for respondents to the
questionnaires prepared.
Step 5: Collect the questionnaires
Researcher collects the questionnaires from
the respondents.
Researcher has to check the number of
questionnaires.

111
Figure 3. 1 pilot study
Table 3. 2 Reliability test of Speaking on Student Motivation and Student Performance
Variables Cronbach’s Alpha Number of items
Teaching Speaking 0.900 N of Items: 20
Student Motivation 0.713 N of Items: 8
Student Performance 0.890 N of Items: 12
Table 3. 3 Mean and Std. Deviation of student motivation and student performance.
3.3. Data Collection and Analysis
The data was analyzed using the (SPSS) tool available in version 23. Designed items are
analyzed statistically and computed the frequency and the individual. Importance of
respondent classification for the lexical scale. Items are spoken through descriptive
statistics.
Table 3. 4 Data Analysis-based Research Hypotheses
Research Hypotheses Collection
Method
Analysis
Method
H0 1. There is no relationship between student motivation
and teaching speaking Questionnaires
Correlation
Coefficient & P-
Value
H0 1.1. There is no relationship between intrinsic and
teaching speaking Questionnaires
Correlation
Coefficient & P-
Value
H0 1.2. There is no relationship between extrinsic and
teaching speaking Questionnaires
Correlation
Coefficient & P-
Value

112
H0 2. There is no relationship between student
performance and teaching speaking Questionnaires
Correlation
Coefficient & P-
Value
H0 2.1. There is no relationship between self-assessment
Correlation
Coefficient & P-
Value
H0 2.2. There is no relationship between peer assessment
Correlation
Coefficient & P-
Value
3. 3.1.1 Data collection timeline
The survey was used to get information on teaching (EFL) speaking in a private secondary
school on student motivation and student performance. The study was provided to the
respondents in a private secondary school in Phnom Penh, Cambodia. The diagnostic
results showed that cross-language speaking practice was proposed within 10 weeks of
recognition. There was one type of speaking chosen from those characteristics that were
responsive in the speaking study. Reply questions and answers. In addition, the teaching
stimulated the speaking activities, especially teaching speaking. Working with illustrates in
detail teaching-driven speaking practices in each speaking task.
Table 3. 5 Present the learning session student’s period.
Participants Procedure
Week 1
Introduction to Responsive of Speaking:
1. Definition of Responsive of speaking
Sample example 1 of Responsive of speaking
Week 2 Special tasks of Responsive
1. Sample example of special tasks of Responsive of
speaking
2. Exercise 1 to work
Week 3 Good responsive speaking elements
1. Sample example 3 of good responsive speaking tasks
2. Exercise 2 to work
Week 4 Steps of the speaking process for Responsive:
1. Pre-Speaking
2. While-Speaking
3. Post-Speaking
4. Correcting

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5. Publish
Week 5 & 6 Task (Topic) to work:
1. Speak about the students preferring (Unit 4, p.55)
(Appendix E)
2. Role play of working in pairs
Week 7 & 8 Task (Topic) to practice:
1. Speak about short phrases (Unit 8, p.103)
2. Role play of short phrases
Week 9 &10 Task (Topic) to work:
1. Speaking about short phrases and formal language
2. Role play of working in pairs
Table 3.5 presents the learning session students period (Lesson plan of speaking on Appendix C).
3.3.1.2 Data collection
The questionnaire was used to get information on (EFL) speaking in a private secondary
school on student motivation and student performance.
3.3.1.3 Research procedure
The research procedures for this research include teaching instruction for students, the
procedure based on the results of the pilot study. However, some of these changes have
been adapted as extrinsic conceptual research. Responses from the pilot study suggest all
changes and improvements as shown in Figure 3.2.

114
Figure 3. 2 Research Procedure
Step 2: Give the questionnaires
Respondents of private schools
Step 3: Answer the questionnaires
Respondents will be asked to complete the
questionnaires.
Step 1: Explain
Giving clear instruction to all respondents
Respondents are questioned to answer the questions related to
learning speaking
Step 4: Do the Peer-Assessment
Respondents observe each other in term of
peer-assessment. Offering the material in the
class with the clear tasks for respondents to the
questionnaires prepared.
Step 5: Collect the questionnaires
Researcher collects the questionnaires from the
respondents.
Researcher has to check the number of
questionnaires.

115
3.3.2 Statistical Results/Tools
Correlation shows how two variables move together in linear mode. In other words,
correlation reflects the linear relationship between two variables. It is an important measure
in data analysis, especially in decision making, predicting market behavior, English
speaking, pattern recognition, and other global issues related to environmental, political,
legal, economic, financial, social, educational, and artistic systems (Xu & Xia, 2011).
Correlation methods are research methods designed to predict the extent of the relationship
between two or more variables. According to Huntsberger and Billingsley (1997), there are
three possible outcomes of related studies: positive correlation, negative correlation, and no
correlation. A positive correlation means that an increase or decrease in one variable is
accompanied by an increase or decrease in another. A correlation coefficient close to 1.00
indicates a positive correlation. Negative correlations: When an increase in one variable is
accompanied by a decrease in another, it means that there is a negative correlation between
those variables. A correlation coefficient close to 1.00 indicates a negative correlation. No
correlation: It is said that there is no correlation when the variables are not correlated and
there is no linear correlation between them. Consecutive coefficients of 0 indicate that they
are not correlated (Mulyaningsih, 2012). Correlation, also called correlation analysis, is a
term used to describe the association or relation between two or more quantitative
variables. This analysis is based on the assumptions of a straight-linear relationship
between quantitative variables. Similar to the measure of association for two variables, it
measures the strength or extent of the relationship between a variable and its direction. The
final result of the correlation analysis is the correlation coefficient; whose values range
from -1 to 1. A correlation coefficient indicates that the two variables are perfectly related
in a positive linear manner; a coefficient of 1 indicates that the two variables are perfectly
correlated in a negative linear manner; a correlation coefficient of zero indicates that no
linear relationship between the two variables is being studied (Gogtay & Thatte, 2017).

116
This research will use correlational research. Correlation studies are to determine the
relationship between the two variables. students' motivation and their achievement in
speaking comprehension (Ningrum & Matondang, 2017).
The characteristics of the correlation analysis are:
0.90 < r < 1.00 Very high correlation
0.60 < r < 0.80 High correlation
0.40 < r < 0.60 Good correlation
0.20 < r < 0.40 Low correlation
0.00 < r < 0.20 Very low correlation
3.3.3 Correlation Coefficient
Correlation coefficients are always between -1 and +1. Closer correlation is +/-1 closer to
perfect linear correlation (Simon, 2005). (i).1.0 to-0.7 The negative correlation is very
negative. (ii). 0.7 to-0.3 Correlation Negative, negative, negative, (iii). 0.3 to +0.3 has little
or no correlation. (iv). From + 0-3 to +0.7, there is a weak positive correlation. (v). Positive
correlation: + 0.7 to +1.0 (Rahman & Deviyanti, 2018).
Relationship coefficients are the important mathematical tool in decision-making.
Compared with decision-making methods using operators, the decision-making method is
based on the coefficients associated with the simple decision-making process (Ye, 2017).
Defining the interpretation of the correlation coefficients is explained in the Interpretation
of Coefficients table below (Faliyanti & Arlin, 2018).
Coefficient Correlation Category
0.800-1.00
0.60-0.799
0.40-0.599
0.20-0.399
0.00-0.199
Very high
High
Enough
Low
Very low

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Correlation coefficients showed significantly higher positive and negative correlations (p
<0.01 level) and also showed significant positive and negative correlations (level <0.05)
(Shinde et al., 2011).
Previous research has suggested that the negative correlation that occasionally exists
between subjective and objective measures is evidence that subjective measures are
incorrect or misleading. Olken (2009), for example, cites a negative correlation between the
amount of money stolen from a road construction project and the perceived amount of
teaching involved in that project as evidence that perceptions are misguided or uninformed.
As a result, it concludes that perceptions should be used with considerable caution when
conducting empirical research and that there is little alternative, but to continue collecting
more objective measures of corruption, however difficult that may be. Seligson (2006)
comes to a similar conclusion, writing that one should be cautious when estimating
corruption based on perception rather than experience because the two may not be closely
related (Jahedi & Méndez, 2014).
Although in the class correlation avoids the problem of a linear relationship being
misinterpreted as an agreement, it does not eliminate other issues associated with
correlation coefficients in this context. It is determined by the measurement range and has
nothing to do with the actual scale of measurement or the size of error that may be
clinically acceptable (Bland & Altman, 1990).
In data analysis and methodological research, the relationship between two variables is
frequently of interest. The aspects of conventional statistical examination of the Pearson's
correlation coefficient further undermine it. Indeed, the standard procedure for challenging
the significance of Pearson's correlation coefficient estimates is sensitive to bivariate
normality deviations. Due to all of the shortcomings of Pearson's correlation coefficient, it
is the proximity of Spearman's to Pearson's correlation coefficient in bivariate normal data,
as well as the suitability of Spearman's statistical test for any type of interval data, It makes
Spearman's correlation coefficient preferable overall. As a result, a statistical test based on
Pearson's correlation coefficient is more likely to be effective for this type of data than
similar tests based on other correlation coefficients. However, the sensitivity of the Pearson

118
product moment correlation coefficient for non-normal data has led to the recommendation
of other correlation coefficients. However, the effect of outline can be reduced by replacing
observations with their ranks. As a result, if the data contains outliers in one or both of the
continuous variables, Spearman's rank-order with the correlation coefficient is preferred
(Chok, 2010).
The Pearson product moment correlation coefficient is a popular way to assess the
relationship between two continuous random variables. As is well known, correlation
should not be confused with causality, as many different causal relationships can be
correlated with the same pair of variables. The use and interpretation of zero-order
correlation networks in the studies has been thoroughly discussed previously (Eisen et al.,
1998; Steuer et al., 2003). Although it is obvious that correlation networks are not the same
as underlying causal networks, correlation can still provide information about the
underlying system. What causal properties can be inferred from studying correlations has
previously been thoroughly investigated (Spirteset al., 1993; Pearl, 2000; Shipley, 2002). In
this paper, we investigate what can be learned from studying correlations in the data sets.
The partial correlation coefficient is the most important concept in this study. A partial
correlation coefficient expresses the relationship between two variables activities when they
are conditioned on one or more other variables (De et al., 2004).
The correlation coefficient, which ranges between -1 and 1, demonstrates the linear
relationship between P-values. The stronger the correlation, the closer the absolute value is
to 1. It should be noted that if two tests are independent, the corresponding correlation
coefficient is 0. However, this is not always the case. The only distinction between
correlation coefficients —1 and 1 is that the former represents a negative correlation. The
former denotes a negative correlation, whereas the latter represents a positive correlation,
direct amount. Used is Pearson's correlation coefficient. Correlation is calculated by
looking at the mutual correlation of exam results (Doanaksoy et al., 2017).
3.3.4 P-Value
Sometimes the P-value is very small, and so it is expressed as P 0.0001 or approx. The
above method can be applied to a small P-value. The P setting equals the value, if it is less,

119
but the statistics will be too small, hence the standard error will be too large and the result
will be too large. This is not a problem as long as we remember that the estimate is better
than the proposed interval. When the researcher was told that P > 0.05 or the difference was
not significant, Things get harder. If the researcher applies the method described here using
P = 0.05, the confidence interval will be smaller. Researchers need to keep in mind that
estimates are lower than the calculated trust intervals (Altman & Bland, 2011).
Significance level information is usually provided in the form of a P-value in
correspondence for most information levels. For example, if P 0.01, then P must be less
than 0.05 (P 0.05). On the other hand, knowing that P 0.05 does not indicate whether P is
also lower than 0.01; therefore, P 0.01 is more informative than P 0.05 if both are true.
Similarly, P > 0.05 is more informative than P > 0.10 if both are true. Among the levels
where significant values of test statistics can be found, the most informative level for
specific results at hand is usually reported. Table 3 gives a reasonable interpretation of the
different P values (Mubashir & Ageel, N.d).
P-VALUE INTERPRETATION
Very strong evidence against H or result is highly significant
0.015 p < 0.05 Moderate evidence against Ho or result is significant
0.05 <_p < 0.10 Strong evidence against H or result is marginally significant
Little or no evidence against Ho or result is not significant
The P-value of the observed value of a test statistic is considered the rule of evidence
against a worthless hypothesis that proves large evidence. In a sense, this is true, but the P-
value is conditional on data from a specific experiment and is therefore relevant for that
particular experiment. If one wants to compare p values from different experiments or even
incorporate evidence into them, as in meta-analysis, one has to consider their distribution
properties (Kulinskaya, 2010).
3.3.5 One-tailed and two-tailed P-Values
A one-tailed test is recommended if the coefficient is relied upon to have a sign (positive or
negative) which should be reflected in the hypothesis that implies the relating association.

120
If no theories are made about the coefficient sign, a two-tailed test is also recommended
(Kock, 2015).
One-tailed versus two-tailed P-values Adair (2013) utilized two-tailed P-values for the
important trial of the speculations, though Adair and Fredrickson (2015) utilized one-tailed
P-values, expressing that doing so was supported because the theories that attribute care
and state care would predict decreased pushed judgment were directional (Nickerson &
Brown, 2016).
3.4 Researcher’s Position
The thesis expression and word choices demonstrated that the EFL students were acutely
aware of their moderate beginner status when compared to local English-speaking
scholastic authors or another setup researcher in the control. Indeed, the most notable
characters the students adopted when they contributed to a diary was beginning scholarly
journalists and inexperienced or junior scientists who were still learning how to speak,
think, and then compose like a specialist or a decent scholarly author. Although these
unpracticed, beginner and student characters were not fixed, on occasion, they were taken
on to determine their learning obligations or potentially to project a confident learning
direction. As demonstrated by the examination of diary themes, as demonstrated, the
understudies would generally criticize their EFL and students’ characters by suggesting to
the difficulties they experienced. Important experiences of this type are linked to issues
about how beginning researchers address the writing of academic documents, which
currently include Ph.D. work and research papers for publication in the academic thesis.
My position, and all participants mentioned at least one significant experience related to
this position, in general terms, I considered the writing task one of the most stressful: It
took me a long time to start writing the thesis. I had a very bad time. It was the most
important challenge. The wide-ranging of important experiences in this category, forcing a
Ph.D. student to adopt and adapt different variations of this educational writer position, was
related to four activities: writing the academic article, presenting the Ph.D, work to a
supervisor, or submitting an article to a thesis or dissertation, getting feedback about this

121
work, and rewriting the previous version of the document according to the suggestions of
the supervisor or the reviewer's comments.
In conclusion, I spent a moment analyzing my practices as a doctoral thesis over the
concepts that I used in my doctoral research. In addition to a positive attitude, completing
the work requires persistency, which is also one of the core ideas of positive psychology. A
hardy working style is important not only during times of hardships and obstacles, but also
during those stages when the research work does not advance quickly. Now and then,
writing is quite good and even boring; furthermore, one needs persistence when explaining
oneself with resource materials, doing a text, and carrying out systematic analyses.
3.5 Summary
This study was conducted at one of the best private secondary schools located in Phnom
Penh City, Cambodia. To achieve an ideal degree of accuracy, almost the whole population
would need to be sampled in little groups. At the point when the population is 500, the
sample size is 100. Sampling is adapted because it enables the researcher to focus on
specific characteristics of the population. The survey was continued after the topic was
verified. Student motivation, Student performance and teaching speaking. Speaking skills
for private secondary schools the level of character is not good. The concepts of this study
methodology, including the design, variables, population, goals, and instrument patterns,
the experimental research, the research procedures, and the data analysis are explained.
Questionnaires were submitted after the topics were covered. Furthermore, SPSS-23 was
used to analyze the data, and the statistics used in this study were the mean and standard
deviation.

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