There are four commonly used nonrandom
designs, each based on a different
consideration, which are commonly used in
both qualitative and quantitative research.
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1- The Convenience Sample
Find some people that are easy to find
the process of including whoever happens to be
available at the time
– Examples:
• Man on the street”
• Agricultural student in the library
• Volunteer samples
• Patient coming to OP
…called accidental” or haphazard (hapˈhazərd)” sampling
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disadvantages…
…difficulty in determining how much of the
effect (dependent variable) results from
the cause (independent variable)
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2. Purposive sampling:
the process whereby the
researcher selects a sample based
on experience or knowledge of the
group to be sampled.
…called judgment” sampling
disadvantages…
…potential for inaccuracy in the
researcher’s criteria and resulting
sample selections.
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3- The Snowball Sample
Typically used in qualitative research.
Recruit one respondent, who identifies others, who identify others,….
1. Find a few people that are relevant to your topic.
2. Ask them to refer you to more of them.
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4- The Quota Sample
روش سهمیه ای
the process whereby a researcher gathers data from
individuals possessing identified characteristics and
quotas
1. Determine what the population looks like in terms of
specific qualities.
2. Create quotas” based on those qualities.
3. Select people for each quota.
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STEP IV Selecting a Sample
Sampling in qualitative research
As the main aim in qualitative enquiries is to explore the
diversity, sample size and sampling strategy do not play a
significant role in the selection of a sample. If selected
carefully, diversity can be extensively and accurately
described on the basis of information obtained even from one
individual.
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STEP IV Selecting a Sample
All non-probability sampling designs – purposive,
judgmental, expert, accidental and snowball – can also be
used in qualitative research with two differences:
– 1. In quantitative studies you collect information from a
predetermined number of people but, in qualitative research, you
do not have a sample size in mind. Data collection based upon a
predetermined sample size and the saturation point distinguishes
their use in quantitative and qualitative research.
– 2. In quantitative research you are guided by your desire to select a
random sample, whereas in qualitative research you are guided by
your judgement as to who is likely to provide you with the ‘best’
information.
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STEP IV Selecting a Sample
The concept of saturation point in qualitative research
In qualitative research data is usually collected to a point
where you are not getting new information or it is negligible –
the data saturation point. This stage determines the sample
size.
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How to Write a Research Proposal
The research proposal in quantitative and
qualitative research:
– In both cases you use a similar structure. The main
difference is in the proposed procedures and methodologies
for undertaking the research endeavor.
– A research proposal’s main function is to detail the
operational plan for obtaining answers to your research
questions. In doing so it ensures and reassures the reader of
the validity of the methodology for obtaining answers to
your research questions accurately and objectively.
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How to Write a Research Proposal
A research proposal must tell you, your
research supervisor and reviewers the
following information about your study:
– what you are proposing to do;
– how you plan to find answers to what you are
proposing;
– why you selected the proposed strategies of
investigation.
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Contents of a research proposal
an introduction, including a brief literature review;
theoretical framework that underpins your study;
conceptual framework which constitutes the basis of your study;
objectives or research questions of your study;
hypotheses to be tested, if applicable;
study design that you are proposing to adopt;
setting for your study; (organisation, agency or community in which you will conduct your study)
research instrument(s) you are planning to use;
sampling design and sample size;
ethical issues involved and how you propose to deal with them;
data processing procedures;
proposed chapters of the report;
problems and limitations of the study;
proposed time-frame for the project. (Work schedule)
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Example: 1-Introduction
Suppose that you plan to study the relationship between academic
achievement and social environment. The preamble/introduction
would include the following:
– The role of education in our society.
– Major changes in the philosophy of education over time.
– Factors affecting attitudes towards education.
– The development of education in … (country).
– Trends in education participation rates in … (country) with particular reference to
the
– region in which the study is being carried out.
– Changing educational values.
– Role of parents and peers in academic achievement.
– Impact of social environment on academic achievement.
– etc.
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2- Problem
What theories have been developed to explain the relationship between
academic achievement and social environment?
What is the relationship between educational achievement and social
environment: what theoretical model will be the basis of your study?
What do previous theories and researches have to say regarding the
components of the theoretical model and academic achievement? For
example, the relationship between academic achievement and:
– — the self-esteem and aspirations/motivation of a student;
– — peer group influence;
– — parental involvement and its relationship with their socioeconomic status;
– — the motivation and interest of students in the subject;
– — employment prospects;
– — relationship with a teacher;
– — etc.
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3- Objectives of the study
Main objective:
To examine the relationship between academic
achievement and social environment.
Sub-objectives:
– 1. To find out the relationship, if any, between self-esteem and a
student’s academic achievement at school.
– 2. To ascertain the association between parental involvement in a
student’s studies and his/her academic achievement at school.
– 3. To examine the links between a student’s peer group and
academic achievement.
– 4. To explore the relationship between academic achievement and
the attitude of a student towards teachers.
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Hypotheses to be tested
H1 = A student’s self-esteem and academic achievement at
school are positively correlated.
H2 = The greater the parental involvement in a student’s
studies, the higher the academic achievement.
H3 = A student’s attitude towards teachers is positively
correlated with his/her academic achievement in that
subject.
Hi = etc.
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4- Study design
It is proposed that the study will be carried out in two government high schools in the
metropolitan area. The principals of the schools most accessible to the researcher will be
contacted to explain the purpose of the study and the help needed from the school, and
to seek their permission for the students to participate in the study. As the constraints of
time and resources do not permit the researcher to select more than two schools,
negotiations with other schools will cease when two schools agree to participate in the
study. It is proposed to select Year 9 students as the academic achievement of students
in Years 8 and 10 could be affected by factors unique to them. Year 8 students may be
experiencing anxiety as a result of having just made the transition to a new system. The
motivation of students in Year 10 could be affected by their being at the stage in their
education where they must decide if they will stay on at school. In order to control the
variance attributable to the gender of a student it is proposed to select only male
students. Once the principal of a school agrees to allow the study to be carried out, the
researcher will brief the teacher in charge about the study and its relevance, and will
arrange a date and time for administering the questionnaire. When the students are
assembled, ready to participate in the study, the researcher will explain its purpose and
relevance, and then distribute the questionnaire. The researcher will remain with the
class to answer any questions the students might have.
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5- Sampling
The selection of schools will be done primarily through
quota sampling. Schools will be selected on the basis of
their geographical proximity to the researcher. The
researcher will prepare a list of schools, in rank order, of
accessibility. Once two schools agree to participate in the
study, negotiations with other schools will cease. All Year
9 male students will constitute the study population. It is
expected that the sample will not exceed 100 students.
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Considering Ethical Issues in Data Collection
Ethical issues to consider concerning Research
participants:
– Collecting information
– Seeking consent
– Providing incentives
– Seeking sensitive information (questions on marital status,
drug use and shoplifting …)
– The possibility of causing harm to participants (not only
hazardous medical experiments but also any social research that might involve such things as
discomfort, anxiety angˈzī-itē, harassment, invasion of privacy, or demeaning procedures)
– Maintaining confidentiality
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Considering Ethical Issues in Data Collection
Ethical issues to consider relating to the
researcher:
– Avoiding bias
– Provision or deprivation of a treatment
– Using inappropriate research methodology
– Incorrect reporting
– Inappropriate use of the information
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Considering Ethical Issues in Data
Collection
Ethical issues regarding the sponsoring
organisation
– Restrictions imposed by the sponsoring
organisation (They may select the methodology, prohibit the publication
of ‘what was found’ or impose other restrictions on the research that may stand in
the way of obtaining and disseminating accurate information.)
– The misuse of information (such as using your research
for justifying management decisions when the research findings do
not support them)
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Processing data
Part one: Data processing in quantitative
studies
1. Editing
2. Coding
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Processing data
Editing: The first step in processing your data is to ensure
that the data is ‘clean’ – that is, free from inconsistencies
and incompleteness.
Editing consists of checking the completed research
instruments to identify and minimise, as far as possible,
errors, incompleteness, misclassification and gaps in the
information obtained from the respondents.
There are two ways of editing the data:
– 1. examine all the answers to one question or variable at a time;
– 2. examine all the responses given to all the questions by one
respondent at a time.
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Processing data
Coding
The method of coding is largely dictated by two
considerations:
– 1. the way a variable has been measured (measurement
scale) in your research instrument (e.g. 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.
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For coding quantitative and qualitative data in
quantitative studies you need to go through the
following steps:
– Step I developing a code book;
– Step II pre-testing the code book;
– Step III coding the data;
– Step IV verifying the coded data.
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Developing a code book
– A code book provides a set of rules for
assigning numerical values to answers obtained
from respondents.
Pre-testing the code book
Once a code book is designed, it is important to pre-test it for any
problems before you code your data. A pre-test involves selecting a
few questionnaires/interview schedules and actually coding the
responses to ascertain any problems in coding.
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Coding the data
– There are three ways of doing this:
• 1. coding on the questionnaires/interview schedule
itself, if space for coding was provided at the time of
constructing the research instrument;
• 2. coding on separate code sheets that are available
for purchase;
• 3. coding directly into the computer using a program
such as SPSS, SAS.
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Verifying the coded data
– Once the data is coded, select a few research
instruments at random and record the responses to
identify any discrepancies in coding. Continue to verify
coding until you are sure that there are no
discrepancies.
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Developing a frame of analysis
A frame of analysis should specify:
– which variables you are planning to analyse;
– how they should be analysed;
– what cross-tabulations you need to work out;
– which variables you need to combine to construct your major
concepts or to develop indices (in formulating a research problem
concepts are changed to variables – at this stage change them back
to concepts);
– which variables are to be subjected to which statistical procedures.
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Analyzing quantitative data manually
– Coded data can be analysed manually or with the help
of a computer. If the number of respondents is
reasonably small, there are not many variables to
analyse, and you are neither familiar with a relevant
computer program nor wish to learn one, you can
manually analyse the data.
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Part two: Data processing in qualitative studies
How you process and analyze data in a qualitative study depends
upon how you plan to communicate the findings.
Broadly, there are three ways in which you can write about your
findings in qualitative research:
(1) developing a narrative to describe a situation, episode, event or
instance; (there is no analysis per se)
(2) identifying the main themes that emerge from your field notes or
transcription of your in-depth interviews and writing about them,
quoting extensively in verbatim format; (content analysis)
(3) quantify the main themes in order to provide their prevalence and
thus significance. (content analysis)
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– Content analysis
• Content analysis means analysing the contents of
interviews or observational field notes in order to
identify the main themes that emerge from the
responses given by your respondents or the
observation notes made by you.
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Part two: Data processing in qualitative studies
– Content analysis involves a number of steps:
1. Identify the main themes
2. Assign codes to the main themes
3. Classify responses under the main themes
4. Integrate themes and responses into the text of your
report.
Content analysis in qualitative research – an example (it
will be explained after the semester)
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More specific steps in the analysis
process
Exploring the database
Coding the data
Developing findings - a description and themes
(Re) presenting the description and themes
Interpreting the findings
Validating the findings
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How do we first explore the database?
Obtain a general sense of the data
Write down memos on hard copy
Think about the organization of the data
Consider whether more data are needed
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How do we divide the text into
segments? (actual coding)
Transcribe the interview
Initially read through for general meaning
Determine coding frame (sentence, paragraph, or phrase) and
determine what the person is saying in the coding frame
Assign code labels in left margin
– Use participants’ words as codes, when possible (in vivo codes)
– Do not over code (10-15 codes per 20 pages)
– Stay away from interpreting comments
Look for overlap among codes
Combine codes into 5-7 themes
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Here is an example of several codes applied to data from an interview
transcript in which a high school senior describes his favorite teacher. The
codes are based on what outcomes the student receives from his mentor. Note
that one of the codes is taken directly from what the participant himself says
and is placed in quotation marks – this is called an In Vivo Code:
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A code can sometimes summarize or condense data, not
simply reduce it. In the excerpt below, a mother describes
her teenage son’s troubled school years. The codes emerge
from the perspective of middle- and junior high school
years as a difficult period for most youth. They are not
specific types of codes; they are first impression” phrases
derived from an open ended process called Initial Coding:
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Themes are broad categories of information (codes
grouped together)
Themes can describe a setting
Themes can describe what occurred
In the 5-7 themes, have some be: a) what you would
expect; b) what you would not expect (unusual
themes)
Themes can also be related (chronology, grounded
theory model)
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Let’s practice the coding procedures using a
sample two-page transcript on the topic of
how department chairs balance their
personal and professional lives.
Qualitative Data Analysis, pp:179-Sharan
B. Merriam
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There are four ways of communicating and
displaying the analyzed data:
– 1. text;
– 2. tables;
– 3. graphs; and
– 4. statistical measures
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Writing a research report
The last step in the research process is writing the research report.
In a way, writing your report is the most crucial step in the research
process as it communicates the findings to your research supervisor
and readers.
A badly written report can spoil all the hard work you have put into
your research study.
Styles of research writing vary markedly among researchers but all
research reports must be written clearly and concisely.
There are different ways of referencing and of writing a bibliography.
You need to select the system that is acceptable to your discipline and
university
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Writing in quantitative and qualitative research differs to
the extent that in qualitative research your style is
descriptive and narrative, whereas in quantitative research,
in addition to being descriptive, it is also analytical and
every assertion is supported by empirical evidence
gathered through the investigation.
Before you start writing the research report, develop an
outline of the different chapters and their contents.
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Mixed methods Research
Often, it is better to use more than one method….
– Quantitative data can reveal generalizable information for a large group of
people
– These data often fail to provide specific answers, reasons, explanations or
examples
– Qualitative research provides data about meaning and context regarding
the people and environments of study
– Findings are often not generalizable because of the small numbers &
narrow range of participants
– Both methods have strengths and weaknesses
– When used together, these methods can be complimentary
اسلاید 359
Mixed methods research is a methodology for conducting research that
involves collecting, analyzing, and integrating (or mixing) quantitative
and qualitative research (and data) in a single study or a longitudinal
program of inquiry. The purpose of this form of research is that both
qualitative and quantitative research, in combination, provide a better
understanding of a research problem or issue than either research
approach alone.
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In this approach, a researcher collects both quantitative and qualitative
data, analyzes them separately, and then compares the results to see if
the findings confirm or disconfirm each other.
The key idea with this design is to collect both forms of data using the
same or parallel variables, constructs, or concepts. In other words, if
the concept of self-esteem is being measured quantitatively, the same
concept is asked during the qualitative data collection process, such as
in an open-ended interview.
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Types of mixed methods designs
The intent of the strategy is to develop better
measurements with specific samples of populations and to
see if data from a few individuals (in qualitative phase) can
be generalized to a large sample of a population (in
quantitative phase).
For example, the researcher would first collect focus group
data, analyze the results, develop an instrument based on
the results, and then administer it to a sample of a
population.
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1. Simple Random sampling
Advantages
1. Easy to conduct
2. High probability of achieving a representative sample
3. Meets assumptions of many statistical procedures
Disadvantages
1. Identification of all members of the population can be
difficult
2. Contacting all members of the sample can be difficult
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Systematic Random Sample
1. Select a random number, which will be known as
k
2. Get a list of people, or observe a flow of people
(e.g., pedestrians on a corner)
3. Select every kth person
– If every Kth person on the list is, say, rich” or senior” or some
other consistent pattern, avoid this method
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Example, to select a sample of 25 dorm rooms in your college dorm,
makes a list of all the room numbers in the dorm. For example there
are 100 rooms, divide the total number of rooms (100) by the number
of rooms you want in the sample (25). The answer is 4. This means
that you are going to select every fourth dorm room from the list. First
of all, we have to determine the random starting point. This step can be
done by picking any point on the table of random numbers, and read
across or down until you come to a number between 1 and 4. This is
your random starting point. For instance, your random starting point is
"3". This means you select dorm room 3 as your first room, and then
every fourth room down the list (3, 7, 11, 15, 19, etc.) until you have
25 rooms selected.
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Stratified Random Sample
طبقه ای
1. Separate your population into groups or strata”
according to some criterion, such as geographic
location, grade level, age, or income.
2. Do either a simple random sample or systematic
random sample from there
a. Note you must know easily what the strata” are
before attempting this
b. If your sampling frame is sorted by, say, school
district, then you’re able to use this method
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Selecting Random Samples
Stratified random sampling (continued)
– Advantages
• More accurate sample
• Can be used for both proportional and non-proportional
samples
• Representation of subgroups in the sample
– Disadvantages
• Identification of all members of the population can be
difficult
• Identifying members of all subgroups can be difficult
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Cluster sampling
خوشه ای
1. Get a list of clusters,” e.g., branches of a
company
2. Randomly sample clusters from that list
3. Have a list of, say, 10 branches
4. Randomly sample people within those branches
a. This method is complex and expensive!
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