The document outlines the processes and methodologies for writing a research report, focusing on both qualitative and quantitative research methods. It includes detailed sections on report organization, data analysis techniques, coding, and thematic analysis, providing insights into statistical methods and qualitative analysis frameworks. Various types of coding, data collection procedures, and statistical tests such as parametric and non-parametric tests are discussed, emphasizing the importance of proper methodology in research.

1. Presented by : TAHIRA RAFIQ
Reg. # 161-FSS/PHDEDU/F19
Presented to : Dr. SHAMSAAZIZ

2. The Research Report
 Why write a report?
 The writing process (pre-writing, composing, re-writing)
 Audience
 Style and tone
 Organizing thoughts
 Back to the library
 Plagiarism

3. Qualitative and Quantitative Research
Qualitative research involves the use of qualitative data
such as: Interviews, Documents, Observation data .
In qualitative research different type of approaches, methods
and techniques are used.
Quantitative research is an inquiry to identify the problem,
based on testing a theory, measured with numbers and
analyzed using statistical techniques

4. Qualitative Report Writing
Format
1. Preliminary pages
2. Main body of research
3. Reference section
Appendices

5. Preliminary Pages
1. Title page
2. Inner title page
3. Supervisor page
4. Certificate by supervisor page
5. Dedication page
6. List of abbreviations
7. List of tables
8. List of appendices
9. Acknowledgment
10. Declaration
11. Abstract
12. Table of contents/content list

6. Main Body of Report
Introduction
 Rationale of study
 Statement of problem
 Objective of the study
 Hypothesis of the study
 Significance of the study
 Delimitation of the study
 Operational definitions of the major terms
 Conceptual framework

7. Literature Review
 Relevant previous researches with APA style
ResearchMethodology
 Research design
 Population of study
 Sample and sampling technique
 Data collection tool development
 Pilot testing
 Data collection procedure

8. Data analysis and interpretation
 ANALYSIS means the ordering, manipulating, and
summarizing of data to obtain answers to research questions.
Its purpose is to reduce data to intelligible and interpretable
form so that the relations of research problems can be
studied and tested.
 INTERPRETATION gives the results of analysis, makes
inferences pertinent to the research relations studied, and
draws conclusions about these relations.

9. Data Analysis
 In general terms it is the description of the strategies
which a researcher used for data analysis. Researcher will
specify the data will be analyzed by manually or by
computer. For computer analysis, identify the program and
the statistical procedures to perform for the data and also
identify the main variables for cross tabulation.

10. STEPS IN DATA PROCESSING
STEPS IN QUANTITATIVE STUDIES
Developing a code book
Pre-testing the code book Developing a Frame of Analysis
Coding the data
Verifying the coded data Computer Manual
1. SPSS x
2. SAS
Raw
data Editing Coding Analysis

11. STEPS IN QUALITATIVE STUDIES
Interviews Developing themes Content analysis
Questionnaires
Observations
In-depth/focus group interviews Computer Analysis: Manual
Secondary sources Thematic
Ethnograph Analysis
Raw
data
Editing Coding Analysis

12. Qualitative Data Analysis
 Case study (intrinsic, instrumental, multiple)
 Thematic analysis
 Grounded theory
 Discourse analysis
 Domain analysis (cultural, folk, mixed)
 Phenomenological analysis
 Coding (open, axial, selective)

13. Qualitative Data Analysis cont…
 Narrative analysis (narrative style, narrative inquiry)
 Content analysis
 Historic analysis
 Ethnographic analysis
 Framework analysis
 Parametric statistics (T-test, Z-test etc.)
 Non-parametric statistics (Chi-square)

14.  STATISTICS is simply a tool in research. In fact,
according to Leedy (1974:21), statistics is a language
which, through its own special symbols and grammar, takes
the numerical facts of life and translates them meaningfully.
 Statistics thus gathers numerical data. The variations of
the data gathered are abstracted based on group
characteristics and combined to interpretation, serve
the purpose of description, analysis, and
possible generalization. According to McGuigan (1987), in
research, this is known as the process of concatenation
where the statements are “chained together” with other
statements.

15. Types of Statistics
There are two types of statistics:
 DESCRIPTIVE STATISTICS – allows the researcher to
describe the population or sample used in the study.
 INFERENTIAL STATISTICS – draws inferences from
sample data and actually deals with answering the research
questions postulated which are, in some cases, cause and effect
relationship.

16. DESCRIPTIVE STATISTICS
Descriptive statistics describes the characteristics of the
population or the sample. To make them meaningful,
they are grouped according to the following measures:
 Measures of Central Tendency orAverages
 Measures of Dispersion or Variability
 Measures of Non-central Location
 Measures of Symmetry and/orAsymmetry
 Measures of Peakedness or Flatness

17. Measures of Central Tendency or Averages
These include the mean, mode and the median.
The mean is a measure obtained by adding all the values
in a population or sample and dividing by the number of
values that are added (Daniel, 1991:19-20).
The mode is simply the most frequent score of scores
(Blalock, 1972:72).
The median is the value above and below which one half
of the observations fall (Norusis, (1984:B- 63)

18. Measures of Dispersion or Variability.
These include
deviation, and
the variance, standard
the range. According to
Daniel (1991:24), a measure of dispersion
conveys information regarding the amount
of variability present in a set of idea.

19. The VARIANCE is a measure of the dispersion of the set of scores.
It tells us how much the scores are spread out. Thus, the variance is a
measure of the spread of the scores; it describes the extent to which
the scores differ from each other about their mean.
STANDARD DEVIATION thus refers to the deviation of scores
from the mean. Where ordinal measures of 1-5 scales (low-high)
are used, data most likely have standard deviations of less than
one unless, the responses are extremes (i.e., all ones and all
fives).
The RANGE is defined as the difference between the highest and
lowest scores (Blalock, 1972:77)

20. Measures of Non-CentralLocation
 These include the quantiles (i.e. percentiles, deciles, quartiles).
The word percent means “per hundred”. Therefore, in
using percentages, size is standardized by calculating the
number of individuals who would be in a given category
if the total number of cases were 100 and if the proportion
in each category remains unchanged. Since proportions
must add to unity, it is obvious that percentages will sum
up to 100 unless the categories are not mutually exclusive
or exhaustive (Blalock, 1972:33)

21.  Measures of Symmetry and/or Asymmetry
These include the skewness of the frequency
distribution. If a distribution is asymmetrical and the
larger frequencies tend to be concentrated toward the low
end of the variable and the smaller frequencies toward the
high end, it is said to be positively skewed. If the
opposite holds, the larger frequencies being concentrated
toward the high end of the variable and the smaller
frequencies toward the low end, the distribution is said to
be negatively skewed (Ferguson and Takane, 1989:30).

22.  Measures of Peakedness or Flatness
Measures of Peakedness or Flatness of one
distribution in relation to another is referred to as
Kurtosis. If one distribution is more peaked than
another, it may be spoken of as more leptokurtic. If it
is less peaked, it is said to be more platykurtic.

23. NON-PARAMETRIC TESTS
Two types of data are recognized in the application of statistical
treatments, these are: parametric data and non-parametric
data.
 Parametric data are measured data and parametric statistical
tests assume that the data are normally, or nearly normally,
distributed (Best and Kahn, 1998:338).
 Non-parametric data are distribution free samples which
implies that they are free, of independent of the population
distribution (Ferguson and Takane, 1989:431).
The tests on these data do not rest on the more stringent
assumption of normally distributed population (Best and Kahn,
1998:338).

24.  Kolmogorov – Smirnov test - fulfills the function of chi-
square in testing goodness of fit and of the Wilcoxon rank sum
test in determining whether random samples are from the same
population.
 Sign Test - is important in determining the significance of
differences between two correlated samples. The “signs” of the
test are the algebraic plus or minus values of the difference of the
paired scores.
 Median Test - is a sign test for two independent samples in
contradistinction to two correlated samples, as is the case with
the sign test.
 Spearman rank order correlation - sometimes called
Spearman rho (p) or Spearman’s rank difference correlation, is a
nonparametric statistic that has its counterpart in parametric
calculations in the Pearson product moment correlation.

25. The Non-Parametric Tests Available
are:
 Two types of data are recognized in the application of statistical
treatments, these are: parametric data and nonparametric data.
Parametric data are measured data and parametric statistical
tests assume that the data are normally, or nearly normally,
distributed (Best and Kahn, 1998:338). Nonparametric data are
distribution free samples which implies that they are free, of
independent of the population distribution (Ferguson and
Takane, 1989:431). The tests on these data do not rest on the
more stringent assumption of normally distributed population
(Best and Kahn, 1998:338).

26.  APPROPRIATE STATISTICAL METHODS BASED
ON THE RESEARCH PROBLEM AND LEVELS OF
MEASUREMENT
The statistical methods appropriate to any studies are always
determined by the research problem and the measurement scale
of the variables used in the study.
 CHI – SQUARE
The most commonly used nonparametric test. It is
employed in instances where a comparison between observed
and theoretical frequencies is present, or in testing the
mathematical fit of a frequency curve to an observed frequency
distribution.

27. Tests
provides the capability of computing student’s t and probability
levels for testing whether or not the difference between two
samples means is significant (Nie, et al., 1975:267). This type of
analysis is the comparison of two groups of subjects, with the
group means as the basis for comparison.
Two types of T-tests may be performed:
 Independent Samples - cases are classified into 2 groups and a
test of mean differences is performed for specified variables;
 Paired Samples – for paired observations arrange casewise, a
test of treatment effects is performed.

28. What is Coding
 People are constantly talking about it, but what is it?
Code and Instructions
 In the most simplified terms, coding is the act of writing
instructions that computers can understand.
 Having cleaned the data, the next step is to code it.
 Coding is the process of naming or labeling things,
categories, and properties.

29. Types of Coding
1. Open Coding:
a first coding of qualitative data in which a researcher
examines the data to condense them into preliminary
analytical categorize or codes
2. Axial Coding:
A second stage of coding of qualitative data in which a
researcher organizes the codes, links them and discovers key
analytical categories
3. Selective Coding:
A late stage in coding qualitative data in which a
researcher examines previous codes to identify and select data
that will support the conceptual coding categorize that were
developed

30. The Method of Coding
1. The way a variable has been measure (measurement scale) in
your research instrument (if a response to a question is
descriptive, categorical or quantitative)
2. The way you want to communicate the findings about a
variable to your readers
All responses can be classified into the following three
categories:
 Quantitative responses
 Categorical responses
 Descriptive responses-transforming into numerical values
called codes

31. Coding Quantitative/Categorical
(Qualitative and Quantitative) data
For coding quantitative/categorical (qualitative and
quantitative) data you need to go through the following
steps:
 Developing a code book
 Pre-testing code book
 Coding the data
 Verifying the coded data

32. Thematic Analysis
 Thematic analysis is the most common form of analysis in
qualitative research
 It emphasizes pinpointing, examining, and recording patterns
(themes) within data
 Themes are patterns across data sets that are important to the
description of a phenomenon and are associated to a specific
research question
 The themes become the categories for analysis
 Thematic analysis is performed through the process of coding
in six phases to create established, meaningful patterns. These
phases are: familiarization with data, generating initial codes,
searching for themes among codes, reviewing themes, defining
and naming themes, and producing the final report.

33. Doing Thematic Analysis
 Reading and re-reading material until researcher is comfortable is crucial to
the initial phase of analysis. While becoming familiar with material, note-
taking is a crucial part of this step in order begin developing potential codes.
 The second step in thematic analysis is generating an initial list of items
from data set that have a reoccurring pattern. This systematic way of
organizing and gaining meaningful parts of data as it relates to research
question is called coding. The coding process evolves through an inductive
analysis and is not considered to be linear process, but a cyclical process in
which codes emerge throughout the research process. This cyclical process
involves going back and forth between phases of data analysis as needed
until you are satisfied with final themes. The coding process is rarely
completed the first time. Each time, researchers should strive to refine codes
by adding, subtracting, combining or splitting potential codes.

34.  In this phase, it is important to begin by examining how
codes combine to form themes in the data. Researchers
begin considering how relationships are formed between
codes and themes and between different levels of existing
themes. Themes differ from codes in that themes are
phrases or sentences that identifies what the data means.
They describe an outcome of coding for analytic
reflection. Themes consist of ideas and descriptions within
a culture that can be used to explain causal events,
statements, and morals derived from the participants'
stories.
 After finalizing the themes to be discussed, researcher
should name and define themes.

35. Discourse Analysis
What is discourse?
Discourse can be defined in three ways:
1. Language beyond the level of a sentence
2. Language behaviors linked to social practices
3. Language as a system of thought.
Discourse analysis is usually defined as the analysis of
language 'beyond the sentence'. And the analysis of discourse
is typically concerned with the study of language in text and
conversation Written by MaCarthy et al. in An Introduction
to Applied Linguistics. Schmitt, N. 2012

36. Basic ideas in Discourse analysis
 Text analysis ( writing) structure of a discourse speech
events
 Conversation analysis (speaking) Turn-taking The
cooperative principle Background knowledge
Basics of Text Analysis
 Cohesion
 Coherence
 Speech events

37. Cohesion, Coherence and Speech
Events
 Cohesion is the grammatical and/or lexical relationships
between the different elements of a text.
 Coherence is the relationships which link the meanings of
utterances in a discourse or of the sentences in a text.
Cohesion and coherence :
 Cohesion helps to create coherence
 Cohesion does not entail coherence
 Coherence can be made with/out cohesive ties/ devises
 Speech event includes interactions such as a conversation at a
party or ordering a meal. Any speech event comprises several
components. debate, lecture, interview, game, daily routine,
etc.

38. Domain Analysis
A method of qualitative data analysis in which a
researcher describes the structure of a cultural domain. It
is a comprehensive approach for qualitative data
1. Cultural domain (a cultural setting in which people
regularly interact and develop a set shared
understandings or “mini culture” that can be analyzed)
2. Folk domain (a cultural domain based on the categorize
used by the people being studied in the field site)
3. Mix domain ( a cultural domain that combine the
categories of members under study with categories
developed by a researcher for analysis

39. Domain Model
 Represents meaningful conceptual classes in a problem
domain.
 Represents real-world concepts (the problem), not
software components (the solution).
 May be shared by different projects.

40. Narrative Analysis
Both a type of historical writing that tells a story and a
type of qualitative data analysis that presents a
chronologically linked chain of events’ in which
individual or collective social actors have an important
role
 Narrative inquiry is method of investigation of data
collection about the people ordinary lived experiences
 Narrative style it is sometimes called story telling
(Berger and Quency, 2004)

41. Tools of Narrative Analysis
 Path dependency refers to how a unique beginning can
trigger a sequence of events and create a deterministic
path that is followed in the chain of subsequent events.
Types of path dependency
Two types of path dependency
 Self reinforcing (explanation looks at how, one’s set into
motion, events continue to operate on their own, events
in a direction that resist external factors)
 Reactive sequence (focus on how each event respond to
an pre-coding 1

42. Periodization
 Dividing the flow of time in social reality into segments or
periods. A field researcher might discover parts or periods
in an ongoing process (e.g. typical day, yearly cycle)
Historical Contingency
 Analytical idea in narrative analysis that explain a process,
event or situation by referring to the specific combination
of the factors that came together in a particular time and
place

43. GROUNDED THEORY
DESIGNS IN QUALITATIVE
ANALYSIS
 ‘‘Grounded Theory is the study of a concept! It is not a
descriptive study of a descriptive problem’’ (Glaser,
2010). ‘‘Most grounded theorists believe they are
theorizing about how the world *is* rather than how
respondents see it’’ (Steve Borgatti).

44.  A grounded theory design is a systematic, qualitative
procedure used to generate a theory that explains, at a
broad conceptual level, a process, an action, or an
interaction about a substantive topic (Creswell, 2008). The
phrase "grounded theory" refers to theory that is
developed inductively from a corpus of data. ‘‘Grounded
Theory is the most common, widely used, and popular
analytic technique in qualitative analysis’’ (the evidence
is: the number of book published on it) (Gibbs, 2010). • It
is mainly used for qualitative research, but is also
applicable to other data (e.g., quantitative data; Glaser,
1967, chapter VIII).

45. When do you use Grounded
Theory?
 When you need a broad theory or explanation of a
process.
 Especially helpful when current theories about a
phenomenon are either inadequate or nonexistent
(Creswell, 2008).
 When you wish to study some process, such as how
students develop as writers (Neff, 1998) or how high-
achieving African American and Caucasian women’s
career develop.

46. Methods
The basic idea of the grounded theory approach is to read a
textual database and "discover" or label variables (called
categories, concepts and properties) and their
interrelationships. The data do not have to be literally
textual they could be observations of behavior, such as
interactions and events in a restaurant. Often they are in
the form of field notes, which are like diary entries.
Data Collection
 Interviews
 Observations
 Documents
 Historical Records
 Video tapes

47. CORRELATIONANALYSIS
 Correlation is used when one is interested to know the
relationship between two or more paired variables.
According to Blalock (1972:361), this is where
interest is focused primarily on the exploratory task
of finding out which variables are related to a given
variable.

48. Thank You