I have two writing assignments, first one due Friday by 7pm EST and second Sunday by 8pm EST. Only serious inquiries only.
- Qualitative Research
- Annotated Bibliography for Qualitative or Field Research Paper
Chapter 6
Steps of Quantitative and
Qualitative Research Designs
Since the guiding principles and procedures for quantitative and qualitative
research are quite diff erent, the two kinds of research design call for somewhat dif-
ferent knowledge and skills
.
In this chapter, we are going to illustrate more detailed
steps of quantitative and qualitative research designs and some issues to consider
at each step. We will fi rst discuss steps of quantitative research designs. Qualita-
tive research designs will be discussed in the second half of the chapter. If you
have already determined that your study calls for a qualitative research design and
are only interested in qualitative research, skip directly to the section on qualitative
research design.
Quantitative research designs include various methods including surveys, exper-
iments, and content analysis. Since the most commonly used quantitative research
method is questionnaire survey, we will focus on the steps involved in survey
research and assume the discussion will help those interested in other quantitative
research methods as well. Students frequently ask us these questions when design-
ing their survey research projects:
• What are my independent variable and dependent variables?
• How do I select a sample to study from my target population?
• What is an acceptable sample size for survey research?
• How do I turn my concepts into variables in survey questionnaire?
• What are levels of measurement and why do they matter?
The fi rst half of this chapter responds to these questions and relevant issues. In
designing a survey research, the following steps are usually necessary:
Student Research and Report Writing: From Topic Selection to the Complete Paper, First Edition. Gabe T.
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106 Student Research and Report Writing
What Are Your Independent and Dependent
Variables?
The term “independent variable” is commonly used in social sciences to refer to
the cause, or the variable that aff ects the other in a hypothesized relationship. The
term “dependent variable” refers to the eff ects or outcomes in a hypothesized
relationship. For example, let’s consider the research question, “How do relation-
ships with parents aff ect teenagers’ school performance?” Suppose you expect that
teenagers who do not have the typical quarrelsome relationships with their parents
will do better in school than those who have a lot of confl icts with their parents.
The independent and dependent variables are already implied by your research
questions. The independent variable in this example would be relationships with
parents and the dependent variable would be school performance. Similarly, if
your research question is “Are teenagers’ grades negatively aff ected by gravitation
toward social media?” then your independent variable is “gravitation toward social
media” and your dependent variable is “grades.” Since you are likely to have more
than one research questions in your study, you may have multiple independent and
dependent variables.
Sometimes, you may have several independent variables and one dependent
variable, and vice versa. For example, questions such as “Do regular medical check‐
ups, exercise, and suffi cient vegetable intakes reduce the likelihood of cancer?” and
“Do cigarette bans in public buildings and higher cigarette taxes encourage smok-
ers to quit smoking?” have multiple independent variables and a single dependent
variable. On the other hand, a research question on the academic and emotional
eff ects of bedtime reading during early childhood assumes one independent vari-
able (“bedtime reading”) and multiple dependent variables (the various “academic
and emotional eff ects”). It is a good practice to write down your research questions
and label your independent and dependent variables.
When you identify and label your independent and dependent variables, you
should be quite clear in your mind that an independent variable is the cause of the
dependent variable and a dependent variable is the eff ect of the independent varia-
ble. A dependent variable must be able to vary or be aff ected when it is infl uenced
by the independent variable. In another example, if you use education as an inde-
pendent variable and salary as a dependent variable, then, you are anticipating that
the salary of your respondents will change when their level of education changes.
If a variable cannot vary or cannot be aff ected, then it cannot be used as a depend-
ent variable. For example, someone’s race and gender cannot be changed by the
infl uence of other variables; thus, they cannot be used as dependent variables.
In the examples above, abstract concepts such as “relationship with parents,”
“gravitation toward social media,” and “emotional eff ects,” need to be more speci-
fi ed and operationalized into measurable indicators so that you can quantify them.
Operationalization is a step where you identify very specifi c indicators or measures
for your concepts. For example, “relationships with parents” are not something
you can directly observe, but you can use some very specifi c indicators for a good
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Steps of Quantitative and Qualitative Research Designs 107
or a bad relationship. Quantifi able indicators, such as “number of arguments a
teenager had with his/her parents within a month,” “number of times a teenager
received a punishment from parents within a month,” and “number of times a
teenager violated rules set by parents” are all good ways to measure whether a
teen has a good relationship with his/her parents. Or, you can simply ask the teen
respondents to rate the quality of their relationship with parents on a scale of one
to ten. The concept, “gravitation toward social media” is hard to measure itself.
You will need to use tangible measures such as “time spent on social media each
night.” Likewise, the concept “emotional eff ects” can be specifi ed into multiple
questions gauging how happy the child is, how social the child is, or how energetic
and curious the child is, and so on.
How Do You Select a Sample to Study from Your
Target Population?
What group of people or cases is your research about? Do your research questions
concern the general population, a particular group of people, countries, schools,
or other social organizations? The answer to these questions will be your study
population, or target population; the term refers to the group of people or cases
about whom you will conduct your study and to whom you will apply the fi ndings
of your study (Babbie 2013 ). Your population is also the pool of cases from which
you will select a sample, or a subgroup of the cases you will actually study. As you
can imagine, if you select a sample that resembles your population closely, you will
be able to use your fi ndings to tell something about your study population. But
if your sample does not resemble your study population, your ability to use your
study fi ndings to predict the patterns in the study population is limited. Suppose
you have selected a group of students from your university whose average grade is
an A. You know it is unlikely that this sample will refl ect what the average grade is
in your university. The extent to which your sample “looks like” your study popu-
lation is called “ representativeness ”; study fi ndings from a representative sample
can be generalized to the study population. For instance, if a group of spectators
selected by random drawing of numbers happens to have the same demographic
characteristics as the spectators in the entire stadium, this sample will be repre-
sentative of the crowd in the stadium. This means that, if there is more rooting
for Team A in this sample, you can generalize that there will be more support for
Team A among the entire stadium crowd.
How do we select a representative sample? Social science methods teach us that
we can approximate a representative sample by reducing systemic selection biases
in sample drawing process. In general, a selection method which only relies on
random chance is considered as having no systemic selection biases (Babbie 2013 ).
There are a variety of diff erent ways to draw a sample from the study population:
simple random sampling, systematic random sampling, stratifi ed random sam-
pling, cluster sampling, quota sampling, snowball sampling, purposive sampling,
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108 Student Research and Report Writing
and availability sampling. Some of these sampling techniques select participants
using random drawing while others do not. The specifi c steps and details of diff er-
ent sampling strategies are beyond the scope of this book. If you need to refresh
your memory on how to draw a particular type of sample, consult some of the
references listed in this chapter.
In the broadest sense, sampling methods fall within two groups: probability and
non‐probably sampling. In the above list, all the variety of random sampling and
cluster sampling fall in the probability sampling category. Quota sampling, snow-
ball sampling, purposive sampling and availability sampling are non‐probability
sampling. Probability sampling methods select participants based only on random
chance. Sampling theory considers this as the best way to obtain a representative
sample . To use probability sampling methods, you need access to the sampling
frame , which refers to the roster of all units in your study population (e.g., an
approximate list of citizens of a country, a list of residents of a community, a list of
all schools, organizations, student roster, and so on), so that everyone is in the pool
of available subjects and only random chance can determine whether someone is
selected to be included in the sample.
Non‐probability samples are used when researchers do not have access to the
sampling frame, or do not have a clearly identifi able study population (such as
undocumented immigrants, homeless population). Research done by students
like you often has to resort to non‐probability sampling methods simply due to
insuffi cient time and resources. Non‐probability sampling methods are likely to
introduce sampling biases because factors other than random chance will aff ect the
selection process. It is okay to study a non‐probability sample, especially for a small
scale exploratory study, or if you are conducting qualitative research. Just keep in
mind that your fi ndings will have limited generalizability, and the limitation should
be included in the discussion of your fi ndings.
When you select your study population, make sure that you can gain access to
them. If your study population is minors (such as children or juveniles) or people
with limited power (such as prisoners), you may face particular diffi culties obtain-
ing informed consents from guardians or getting permission from the heads of
the institutions to enter the sites to collect data for your research. Therefore, think
carefully about access before you decide to study a particular population.
After you decide on your study population, decide on what your unit of analysis
is. It may be individuals, universities, organizations, or countries, depending on
what is most appropriate for your research.
What Is an Acceptable Sample Size for Surveys?
Another issue to consider is sample size. Regardless of whether you use probability
sampling or non‐probability sampling, the size of the sample is an independent
issue which requires your attention. If you want to conduct surveys and use com-
puter software to do data analysis, you need a suffi cient number of respondents in
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Steps of Quantitative and Qualitative Research Designs 109
your sample. As a general guideline, a minimum of 400 cases will be amenable for
statistical data analysis. This suggestion is to reduce sampling error due to sample
size. If you have a sample size of 400 cases, the standard sampling error will always
be 5% or smaller no matter what the variation is in the study population (Babbie
2013 ). In reality, however, it may be unrealistic for a student researcher to be able to
draw a suffi cient size sample; you are more likely to work with much smaller size
sample due to the time and resource constraints. In deciding your sample size, con-
sult with your project supervisor, or professor, as they may have specifi c guidelines
or requirements for sample size. Generally speaking, three principles are useful in
determining sample size.
First, the larger the sample size, the smaller your standard sampling error will
be. At the same time, your sample is more likely to resemble the characteristics
of your population and you will be able to generalize your fi ndings to the target
population.
Second, if you are conducting a quantitative study, most statistical analysis tech-
niques used in social sciences assume a normal, or the bell‐curve distribution of
data. If the sample is too small, say less than 100 cases, there is a good chance
that you will not meet this assumption of a normal distribution. Our advice is
to obtain at least a sample size of 100 respondents, if you plan to use statistical
analysis techniques. With that number you will be able to use commonly used
descriptive and inferential statistical techniques such as cross‐tabulation analyses,
chi‐square tests, t‐tests for comparison of means, and so on. Keep in mind that you
may receive invalid answers, which you will exclude from your analysis; to obtain a
sample size of a 100 valid cases; you may need to go slightly beyond your targeted
sample size when you collect the data.
Third, a small sample size may produce insignifi cant statistical results simply as
a function of the sample size. Sometimes, small samples require you to use special
statistical measures other than the commonly used measures mentioned above.
According to probability theory, when sample size decreases, the standard error
increases. For example, if you do a chi‐square test with a very small sample, you
may fi nd that many of the cells in your cross-tabulation have fewer than fi ve cases
and your chi‐square value is not statistically signifi cant. If you have more than 25%
of the cells with fewer than fi ve counts, your chi‐square analysis is not acceptable
(George and Mallery 2000 ). If this is the case, you cannot use chi‐squire analysis to
test whether two variables are statistically independent of each other. On the other
hand, if you only intend to use simpler descriptive statistics such as percentages
and graphs to answer your research questions, a sample size smaller than 100 can
still work.
A “robust” sample, or a suffi ciently large and representative sample, is needed
if your goal is to perform an explanatory study. This is one reason why many
researchers turn to secondary data, data collected by governments or large organ-
izations with resources, for explanatory studies. As a student researcher, you are
likely to conduct a study on a modest size sample using non‐probability sampling
strategy. This is quite all right, as long as you are mindful of the limitations of
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110 Student Research and Report Writing
your methods, make cautious interpretation of your fi ndings, and discuss the limi-
tations in your report. If you and your supervisor decide to work with very small
sized sample, say less than 50 respondents, you may consider it as a pilot test for a
full‐scale study at a later time.
A pilot study can yield valuable information; you can get a sense of how survey
questions are interpreted by the respondents, gauge whether the length is appro-
priate, estimate what response rates you can anticipate, and detect any potential
problems in administering the surveys. These are important learning experiences
and skills‐building processes which make your project meaningful. You will also
obtain some descriptive statistics about the patterns of behaviors and attitudes you
are investigating with a pilot study. If you must resort to very small sample sizes
due to the constraints in time and resources, consult your project supervisor about
designing a well‐planned pilot study which will give you a good foundation for
future projects; this can be more meaningful assignment than a poorly conducted
research on a larger sample.
Also, the above guidelines are for quantitative studies requiring statistical data
analyses. Qualitative research is most often done on a sample much smaller than
100. Sampling issues in qualitative research will be further discussed later in the
chapter when we review steps of qualitative research.
How Do You Turn Your Concepts into Variables in
Surveys?
If you conduct a survey, develop your survey questionnaire to include questions on
the independent and dependent variables and demographic information needed
for your research. Most surveys include demographic questions to collect basic
information about your study population. Typically, you ask about gender, age,
level of education, and socioeconomic status, and race and ethnicity. However,
which information is needed depends on your research questions. For example,
if you study university students’ adjustments to campus life away from home, it
may be relevant to ask how many years they have attended university. Before you
start to construct your survey questions, you should have a clearly identifi ed list of
your independent, dependent, and demographic variables. If you have an abstract
concept in your research question, you need to operationalize it as we described
earlier in this chapter. In surveys, most concepts are measured by one or a set of
questions. For example, if your research question is “How do gender, race and age
aff ect student school performance,” then you will need to specify and measure
school performance. In this case, you cannot directly ask your respondents what
their school performances are. Instead, you need to identify indicators such as stu-
dent Grade Point Average, class attendance, or time spent on study to measure
school performances.
Since you need to include measures for all your concepts, you should have at
least one question for each of your concepts in your survey instrument. Some
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Steps of Quantitative and Qualitative Research Designs 111
simple variables such as gender, age, whether or not one is in university, whether
or not one supports a political candidate can be suffi ciently measured by a single
question item. But for abstract concepts, or concepts that have a broader range of
meaning you may need more than one indicator. For example, if your variable is
depression, a composite question consisting of several items asking about diff erent
symptoms of depression may be a far better measure than a single question asking
whether one experiences depression. The key idea is that the indicators in your
survey instrument should capture the full meaning of your concepts.
In the U.S., students in university classes participate in teaching evaluation sur-
veys at the end of the semester. The purpose of the surveys is obviously to measure
“good teaching.” Imagine that schools use just one question, “does your professor
demonstrate good teaching?” This single item indicator would be not only too
simplistic, but also vague as each student may have diff erent ideas about what good
teaching is. Typical teaching evaluation surveys thus include several questions on
various aspects of teaching, such as knowledgeability, organized presentation, use
of examples, interaction with students and so on.
In developing survey questions, please pay attention to the validity of your
questions to make sure that you will get what you intended to get, or measure
what you mean to measure. For example, student GPA is a good indicator of
academic performance. How many times students visit the library may not be a
direct measure of academic performance. It may aff ect academic performance, but
library use itself is not a valid measure of academic performance. Your measures
should also have reliability which refers to the quality of a measure that produces
the same value or observation repeatedly. For example, reading a thermometer is a
much more reliable measure for fever than feeling the temperature with your hand
on someone’s forehead, since the judgment from touching one’s forehead can be
capricious. Likewise, asking “how many times did you drink last week?” is more
reliable measure than asking “do you drink often?”
What Are Levels of Measurement and Why Do They
Matter?
When you design your survey questions, think about what kind of data analysis
you are going to conduct after you collect your data. Statistical data analysis pro-
cedures are often closely related to the mathematical property of your variables,
called “ the levels of measurement .” Given that statistical analyses are based on
mathematical computations of various statistics, the mathematical qualities of the
numerical data you collect will infl uence data analysis.
Let us use a simple example. For the demographic variable, gender, researchers
often give the numeric code “1” for male respondents and code “2” for female
respondents. Computing what percentage of your sample is men or women would
be an appropriate thing to do, but computing the mean score for this variable is
not, as there is no such thing called “average gender.” You would not want to use
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112 Student Research and Report Writing
statistical technique “mean score” when you perform an analysis of variables such
as religion, nationality, or favorite Thai dish. This is because all of these examples
are variables with no mathematical meaning, or no variations in amount. On the
other extreme, if you ask respondents to write down their age, or their salaries in
precise currency terms, then you know that the diff erent values you will obtain can
be compared in quantity; computation of mean scores (e.g., average age, average
income) makes perfect sense with these variables.
These examples illustrate the diff erent mathematical qualities of the numeric
data you will obtain from diff erent types of variables. As we mentioned above,
statistical tests assume that the variables entered into the computation have certain
type of mathematical property. The types of mathematical properties are expressed
in levels of measurement. In statistical analysis, four levels of measurement are
diff erentiated , depending on the level of mathematical precision of the meas-
ure: they are nominal, ordinal, interval and ratio levels. A nominal level of meas-
urement has the lowest level of mathematical quality, because its values are num-
ber codes that do not represent actual quantities, but simply diff erent qualities. A
ratio level of measurement on the other hand is the highest level of mathematical
precision because its values actually correspond to mathematical quantities, just
like natural numbers. Let us explain each of these with some examples.
Nominal level measurements pertain to those variables whose attributes are
simply diff erent in type, not in quantity, from one another. In other words, the
numeric codes assigned to the respondents’ diff erent answers for a nominal level
measure have no implied order or quantity. The diff erence between categories
of a nominal scale is a qualitative diff erence (Graziano and Raulin 1999 ). For
example, gender, race, occupation, and marital status are usually measured at a
nominal level, as diff erent attributes respondents can take in these variables are
diff erent kinds, and not diff erent amount. Here is an example of nominal level of
measurement:
Question: Which of the following racial groups do you identify yourself with?
1 ___African American
2 ___Asian American
3 ___Caucasian American
4 ___Native American
5 ___Hispanic American
6 ___Other
In this case, the number 1 is arbitrarily used to represent the African Ameri-
cans and the number 3 is used to indicate the Caucasian Americans. The numbers
assigned to each category, however, do not mean any higher or lower social value
relative to one another. In addition, the attributes of a nominal level variable are
mutually exclusive (i.e., no overlap between attributes) and exhaustive (i.e., they
cover the full range of possible attributes for all respondents).
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Steps of Quantitative and Qualitative Research Designs 113
Ordinal level measurements assess a variable in its order of magnitude. They
have the property of magnitude (Graziano and Raulin 1999 ), or a rank‐order. In
other words, variables that are measured at the ordinal level have categorical attrib-
utes like those of nominal level measures, but they also have the advantage that
the categories can be ordered or ranked from low to high or from one extreme to
the other. Thus, the numbers assigned to diff erent attributes, or categories signify
diff erent point in a system of order. For example, social class, people’s attitudes, or
students’ academic standing are usually measured at the ordinal level. For example:
Question: Which social class do you belong to?
1 ___Lower class
2 ___Lower middle class
3 ___Middle class
4 ___Upper middle class
5 ___Upper class
6 ___Other
In this example, the respondents’ social class is rank‐ordered from what are
socially deemed lower socioeconomic statuses to higher ones. The numeric codes
do not mean exact quantities, but they indicate a rank‐order between categories
and identities. The diff erence between lower class and lower middle class is not the
same as the diff erence between upper middle class and upper class. Nor, the lower
middle class (Numeric value “2”) is exactly half as rich as the upper middle class
(Numeric value “4”). In order to make it exhaustive, “other” has been provided for
those respondents who are not sure about their socioeconomic status or who do
not want to reveal their socioeconomic status.
The interval level of measurement measures a variable’s attributes that are not
only rank‐ordered but also are separated by a uniform distance between them. Inter-
val level variables are measured in terms of a standard unit of measurement, and
therefore the categories of an interval measurement have equal intervals between
the categories. For example, temperature can be measured on an interval level.
Question: What is the temperature in your city today? ______ C°
In this example, the measurement indicates an equal diff erence between any two
units of the measurement. That is, the diff erence between 18C° and 19C° is the same
as the diff erence between 10C° and 11C°. But notice that there is no absolute zero, or
absence of a value in this scale. A temperature of 0C° is still a temperature indicating
a relative location in this scale, and it does not mean that there is no temperature.
Ratio level of measurement is the same as interval level of measurement, except
that ratio level of measurement is also based on a true zero point or an absence of
the quantity. In other words, variables that are measured on a ratio scale have the
characteristics of an interval scale plus a real zero. For example:
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114 Student Research and Report Writing
Question: How much money do you have in your pocket now? ________ Euros
In this example, if a respondent answered zero, he/she has absolutely no money
at all in his/her pocket. Likewise, if someone answered zero, for a question about
years of schooling, you can interpret this as a lack of any formal schooling.
In social science research, you will fi nd very few, if any, true interval level varia-
bles; most measures are ratio level measures. Also, the distinction between interval
level and ratio level measures does not make a diff erence in statistical computa-
tions for social sciences. Therefore, you may treat interval and ratio level of meas-
urement as if they are the same level. This is a common practice and the reason
why many people lump the two together as “interval‐ratio” level measures. If you
use software packages for statistical analysis such as IBM’s SPSS program, you will
notice that there is no option for designating a variable as an interval level meas-
urement. Instead, interval and ratio levels of measurement are both indicated with
the term “scale.”
Since the levels of measure will enable or limit the statistical procedures you
can apply at a later stage, it is important to understand diff erent levels of meas-
urement at this stage and operationalize your variables into adequate levels of
measurement. For instance, you can use bar charts or pie charts, frequencies and
percentages, and cross tabulations to analyze your data when your independent
and dependent variables are nominal level of measurement. If you want to calcu-
late means, correlations, or multiple regressions, however, your independent and
dependent variables need to be interval or ratio level measures. Similarly, to use
means or variances to compare two groups (t‐test) or several groups (one‐way
analysis of variance), your dependent variables should be measured at the interval
or ratio level. All of these procedures are explained in Chapter 9 .
If you are not sure about what kind of data analysis you will eventually use
at the time you design your survey questionnaire, we advise you measure your
independent and dependent variables at the interval or ratio level whenever poss-
ible. Of course, some variables, such as gender, ethnicity, and religious affi liation,
are naturally nominal level and cannot be measured as ratio level variables. But
there are many variables that can be measured on an ordinal scale or a ratio scale;
for example, salaries from work can be measured as an ordinal level variable
(“Which of the following range does your monthly salary belong to?”) or as a
ratio level variable (“Please write down the exact amount of your monthly salary
in U.S. dollars.”). In this case, we recommend that you use a ratio level variable.
We suggest this because statistical software packages allow you to transform ratio
level variables into a lower level measurement after the data are entered, but you
cannot do the reverse.
For example, if you collected your income data in real dollar amount (i.e., a
ratio level measure) but later decide to use simple cross‐tabulation technique for
analysis, you can recode the income variable into a new four‐ or fi ve‐category
income range variable (i.e. an ordinal level measure). No information is lost in
this recoding process, and you can still keep the data on your original variable (the
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Steps of Quantitative and Qualitative Research Designs 115
exact dollar amount for each respondent) as a separate variable. If your original
variable was an ordinal level income range variable, however, there is no way you
can estimate what exact dollar amount each respondent earned. The lesson here
is that mathematically higher level measures tend to contain more minute infor-
mation. Therefore, you should measure at the highest level possible because you
may need information with more precision at the data analysis stage.
Similarly, it is a good idea to use ordinal level measures when you have an
option to choose either nominal level or ordinal level. For example, students often
construct questions with simple “yes” or “no” answer choices. You may ask yes/
no questions but after collecting data, realize that the procedures you can use to
analyze data are quite limited. Yes/no questions contain far less information than
asking, for instance, about the frequency of particular behaviors or the degree of
support on specifi c issues. To illustrate this point, let’s compare how the following
two questions treat the same variable. When asking about college student drug
use on campus, student researchers have a tendency to ask:
Did you use marijuana on campus last month?
1___Yes 2____No
But rephrasing this into an ordinal level question off ers more precise informa-
tion on each respondent and allows a greater range of statistical analysis proce-
dures later on:
How often did you use marijuana on campus last month?
1 ___ Never
2 ___ Seldom
3 ___ Sometimes
4 ___ Often
5 ___ Always
6 ___ I do not know
You can use even more computer data analysis procedures, if you measure stu-
dent drug use on a ratio level:
How many times did you use marijuana on campus last month? _______
The lesson is that, in designing your questionnaire, consider what kind of data
analysis you will conduct and determine the appropriate level of measurement for
each of your variables. Quantitative research designs are suitable to pursue relatively
straightforward questions and clearly defi ned hypotheses. If your research questions
call for the in‐depth understanding of a complex problem, a deeper examination of
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116 Student Research and Report Writing
a particular experience, or comprehensive knowledge of a group of people, you
will be better off collecting detailed qualitative data for your empirical research. We
now discuss some methodological issues relevant to qualitative research.
There are more issues related to survey designs which we have not covered in this
chapter. Some are beyond the scope of this book. We hope that you will fi nd more
in‐depth details from your research methods textbooks. One issue we have not dis-
cussed in this chapter is how to write clear and eff ective questions. There are many
tips for properly wording survey questions. Essentials of Research Methods: A Guide to
Social Science Research (Ruane 2005 ) has two excellent chapters on this issue.
What Do You Need to Know about Qualitative
Research Designs?
The main benefi t of qualitative data collection methods is two‐fold: 1) they allow
you to obtain data refl ecting the participants’ own perspectives, and 2) they are
conducive for a wide range of observations without a pre‐conceived notion about
the issue or the situation. These methods are designed to have participants’ volun-
tary information in their own words, or let you make observations in the natural
setting of a situation. There are various ways of conducting qualitative research,
and each way may be appropriate for a specifi c type of research project. As we
discussed in Chapter 5 , some of the popular qualitative data collection strategies
include in‐depth interviews, participant observations, ethnography, and focus
groups. Since in‐depth interviews are most frequently used by students, we will
focus on the steps and concerns involved in in‐depth interviews. When designing
their qualitative research project, students frequently ask us these questions:
• How do I construct my interview questions?
• How do I select people for interviews?
• What should I do during the interviews?
• What other qualitative data collection methods can I consider?
The remaining part of this chapter responds to these questions and relevant
issues. In designing in‐depth interviews, the following steps are usually necessary:
How Do You Construct Your Interview Questions?
In‐depth interviews are based on open‐ended questions that reject pre‐conceived
predictions and allow participants to express themselves in their own words. They
off er you a better sense of what respondents feel and think. For example, suppose
you are interested in understanding the challenges new immigrants face in adjust-
ing to their host country, your research question may ask: What are the main prob-
lems new immigrants and refugees face in integrating into their receiving country?
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Steps of Quantitative and Qualitative Research Designs 117
This question is broad and open‐ended. You have no independent or dependent
variables, nor is there a need to identify them.
But you are not approaching this question blindly, either. Having reviewed exist-
ing literature, you probably now know something about issues that concern new
immigrants. For example, you know that the literature on immigrants and refu-
gees discusses legal barriers for work permits and citizenship, cultural diff erences,
language barriers, prejudice and discrimination based on race, ethnicity and reli-
gion, and economic diffi culties. With this knowledge you can identify focal areas in
designing your interview research and constructing interview questions.
In‐depth interviews are not usually highly structured and allow you to spontane-
ously explore emerging new issues and themes as the interview unfolds. To collect
relevant information consistently across many interviewees, however, you need
to have some standard set of broad questions you want to ask everyone. This set
of questions is called an “interview schedule.” Depending on how many specifi c
questions you have in your interview schedule, and how closely you plan to adhere
to the prepared list, you may have highly structured interviews, semi‐structured
interviews, or unstructured interviews. Which one you choose to do depends on
the goals of your study. Here are a few tips for constructing an interview schedule.
First, keep the questions open‐ended and allow your respondents to answer
them in their own way. Questions leading to a yes/no answer are not productive
in interviews. For example, if you ask, “Did you have a lot of diffi culty fi nding jobs
after you came to this country?” your interviewee is likely to simply answer, “Yes, I
did” and stop. Instead, if you ask, “Tell me about how you tried to fi nd work when
you arrived here, and how you found your fi rst job,” you are inviting your respon-
dent to tell you about his experiences and diffi culties with his job search.
Some immigrants may not have had much trouble fi nding a job because a rel-
ative had a job off er already lined up while others lacking connections may have
had a long period of unemployment and economic hardship. Respondents may
voluntarily tell you stories about demanding and unrewarding work or share their
experiences of discrimination while telling their job search stories. You will be in
a better position to collect these varying accounts on work when you use open‐
ended phrases like the second example. Moreover, you will be able to assess what
issues matter most about work and job opportunities by paying attention to the
issues your interviewees mention fi rst. These rich details will not surface, if you
ask “yes/no” questions.
Second, for each interview question, you should think about “probing ques-
tions,” or follow‐up questions for more details or information on a closely related
issue. Probing is a critical skill in interviews since your respondents are unlikely to
pour out details about their experiences right from the beginning. In our exam-
ple about the job experience, your interviewee may give you a relatively short
answer, for instance, “I have a friend who worked in a restaurant. He told me that
his manager may give me a job. So, I went with him one day and started to work
there.” At this point, probe further in a few diff erent directions. If your research
focus is job market conditions and opportunities for immigrants, ask follow‐up
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118 Student Research and Report Writing
questions about how long he searched before getting his fi rst job, what he did
to search for work, what types of job were available, and so on. If your research
question concerns the role of social networks for new immigrants, which many
scholars consider important, then probe more about this “friend” who helped your
interviewee. Ask: “Who was that friend? Was he someone from your country? Did
you meet him here?” These questions will help your respondent elaborate on his
relationship with his friend and possibly other resources and information that may
be vital to his settlement into a new community.
Third, keep your interview schedule fl exible enough so that you can pursue new
leads or switch the order of your questions as the conversation unfolds. Depending
on your research questions and style, interviews can be more or less structured.
You may feel skilled and comfortable conducting interviews only with an outline
of themes in mind (unstructured interviews). Or you may be concerned about
forgetting some important questions and would prefer to stay close to the prepared
interview schedules (structured interviews).
Most of you are likely to be somewhere in‐between and follow a style called
“semi‐structured” interviews. In semi‐structured interviews, you will prepare an
interview schedule including some probing questions but you will also let yourself
explore a diff erent theme or direction as new clues emerge in the conversation. Be
ready to embrace newly found issues in the course of the interview. In our exam-
ple of an interview an immigrant, suppose your interviewee told you that one
day he got into a fi ght with another immigrant worker from Africa because he did
not like the African song this man was singing to himself all the time. This may
prompt you to ask about possible confl icts between workers of diff erent ethno‐
religious backgrounds in places where many immigrants work. Even if you had
not planned to ask questions about this issue, this may well be a relevant focal
point in your research. Do not hesitate to add new questions, if there are newly
emerging interests as your interviews progress.
Fourth, interviews are conversations and the fl ow of the interviews may aff ect
the amount of information you obtain. In general, it helps the interviewees’
thought process if you move from one question to the next with a sense of con-
tinuity. For instance, ask about marriage and then about children; there is a close
connection between these themes and your interviewee is likely to stay focused
and remember more relevant details. But if you ask about marriage fi rst, then edu-
cational background and old school friends, and fi nally about children, it may be
diffi cult for your respondent to recall information, jumping back and forth on dif-
ferent periods of the life course. This consideration of smooth fl ow can guide you
in constructing your interview schedules as well as carrying out the interviews.
Overall, having a prepared interview schedule/guide off ers at least two advan-
tages. One is that you will have a clear direction before you start your interviews
and will be able to include all your main questions. The other is that you are likely
to collect consistent information from all interviewees. As long as you use the
interview guides with fl exibility, you will enhance your chance of obtaining richer
and more useful data.
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Steps of Quantitative and Qualitative Research Designs 119
How Do You Select People for Interviews?
Just as in quantitative research, qualitative studies require careful thoughts on
sample selection. Selecting appropriate participants for your research will have an
impact on your data, since you are likely to have a relatively small sample in an
in‐depth qualitative study. It may be unrealistic to gather a sample that is truly
representative of your study population. One way to handle this limitation is to
attempt to select a sample heterogeneous enough to capture the diversity of expe-
rience and perspective on the topic you are studying. Selecting interviewees par-
allels the process of sampling in quantitative research, except that you are highly
likely to rely on a non‐probability sampling methods. When you select participants
for an interview‐based study, carefully consider the heterogeneity of the sample, a
minimum threshold of interviewees, and the pros and cons of diff erent sampling
methods.
For the heterogeneity of your sample; you probably want to select people of
diff erent demographic attributes so that you can collect accounts from diverse
points of views. For example, if you want to know about experiences of homeless
people in a large city, include people of diverse demographic backgrounds, with
dependent families and without, from diff erent service facilities, and from diff erent
geographic sections of the city. Beginning researchers often resort to interviewing
those who are easily available. But if you select people solely in the same social
network, you may obtain similar stories; people in the same social network tend to
have common characteristics. It is convenient to recruit participants from one sup-
port group for recovering alcoholics, for instance, but this is not the best strategy
for discovering a range of patterns, experiences, and stories about this population.
To increase the heterogeneity of your sample, you may purposively reach out to
participants with diverse social characteristics (e.g., by gender, age, nationality, eth-
nicity, level of education, race, and so on). Selecting participants from multiple
geographic locations is one strategy to increase diversity and improve representa-
tiveness within your sample.
Second, even if a relatively small size sample is acceptable for qualitative stud-
ies, you need to have a minimum number of participants in your sample. If you
interview only four or fi ve individuals, you will be unable to fi nd enough common-
alities, or draw any generalizable conclusions. In this case, the outcomes become
little more than fi ve individual stories. If you are conducting a case study which
is the type of qualitative study zooming into comprehensive and in‐depth exam-
ination of a single or a few cases, then fi ve individual stories would still be good
for case‐oriented analyses. But if your objective is to summarize some theoretical
themes, then you may need a minimum number of interviews to represent a com-
mon story of your study population.
The sample size in qualitative research is usually not pre‐determined, but a
“ saturation point ” is often recommended as the threshold for concluding the
data collection. The “saturation point” is defi ned as the moment when additional
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120 Student Research and Report Writing
interviews no longer produce new information (Schutt 2011 ); in other words,
when you feel that you are getting the same story over and over, you may have
reached the saturation point. The saturation point can come earlier or later in your
interviews depending on the scope of your research questions and the heteroge-
neity of your sample.
In our experiences, published qualitative studies tend to be based on more than
30 interviews and rarely go over a sample size of 100 unless secondary data sources
were used. For semester‐long undergraduate research, we think a minimum of
13–15 interviews is realistic, although you may not be able to reach the saturation
point. If you have a year or longer to work on your research project, you will have
a better chance of getting enough interviews to reach the saturation point. We
strongly recommend that you consult with your project supervisors and faculty
mentors who are familiar with the scope of your research questions and the con-
straints of resources under which you are conducting this research. Since most
qualitative research is exploratory in nature, a non‐representative sample itself
does not disqualify the entire research. You just need to interpret its fi ndings with
caution and avoid an overgeneralization.
A few non‐probability sampling methods you may want to consider, instead of
availability sampling, include purposive sampling, quota sampling, and snowball
sampling. Purposive sampling involves selecting individuals who fi t certain crite-
ria required by your research questions. Individuals may be selected because they
belong to certain groups, demographic categories, or they are likely to have special
information or knowledge that can help your study. For example, for a study of a
city’s sanitation services, you may purposively interview people involved in sanita-
tion services including the city’s director, managers of various divisions, and street
cleaning crew. If it is important for you to include in your study various types of
business organizations, you may also set a quota in your sample for various busi-
ness categories and reach out to diff erent business sectors according to your quota.
This strategy is called “ quota sampling .” Quota sampling helps you to ensure a
representation of various groups of cases in your sample and to achieve a make‐up
of the sample that is similar to your target population. Snowball sampling is a
strategy to recruit additional participants by utilizing referrals from earlier inter-
views. Just as a snowball grows in size by rolling it, you will rely on earlier inter-
viewees to introduce new participants to you. In snowball sampling, you will ask
each interviewee to introduce you to other potential participants who meet the
criteria to be included in the study; the chain of referrals will enable you to amass
growing numbers of people into your study, like an enlarging snowball. Snow-
ball sampling is especially useful if your study population is not easily identifi able,
such as homeless people, victims of intimate partner violence, or undocumented
immigrants.
Although non‐probability sampling methods are commonly used in qualitative
research, this does not mean that you cannot use a probability sampling strategy.
If your study population is a small sized group and if you have a list of every-
one in the population, you can pursue the random sampling strategy described
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Steps of Quantitative and Qualitative Research Designs 121
earlier in this chapter. For instance, if you are conducting a case study of a busi-
ness corporation and you can obtain the list of all employees in this organization,
make a random selection using the list. But bear in mind that if the sample size
is small, random sampling does not promise any greater representativeness than
the non‐probability sampling strategies listed above. You may even use a combi-
nation of diff erent selection strategies to recruit participants for your qualitative
study. The key is to select a group of participants from which you will be able to
collect a maximum amount of information for your research questions. Thus, the
scope and goals of your research will determine the best methods for selecting
interviewees.
What Should You Do to Have Productive Interviews?
Since interview research entails face‐to‐face interactions between the interviewer
(you) and the interviewees, paying special attention to the following aspects of the
interviewing process will positively aff ect the quality of your interviews.
First, you want to have a nice introduction. A proper and friendly introduc-
tion will set the tone of the entire interview. One of the main purposes of the
introduction would be to create a comfortable connection with the interviewee.
There is another important purpose, which is to ensure ethical process. During
the introduction, you need to let the interviewee get to know you and learn about
your study, so that he/she feels comfortable in agreeing to participate. Here are
some very important things to include in your introduction: information about
yourself (e.g., your name, institutional affi liation, position/title etc.), infor-
mation about your study (e.g., the objectives, sponsorships of the study if any,
the purpose of the interview, the use of data, etc.), and information about the
recruitment of the respondent (e.g., how his/her name was found, why he/she
was selected, what makes him/her a suitable interviewee for the study, etc.), and
what you will ask of the respondent (e.g., types of question to discuss, length
of the interview). Following the research ethics protocol, you should also clarify
whether the interview will be tape‐recorded, how confi dentiality is guaranteed,
and how you will safeguard the information collected. If there is a potential emo-
tional risk for the interviewee, you must disclose it before he/she agrees to sign
the informed consent statement. Also, ask the interviewee if she has any other
questions about the study.
Openness and respect are important guiding principles in this process. Remem-
ber that the participant is doing you a huge favor by agreeing to give you his/her
time sharing personal experiences and thoughts. At the same time, this favor puts
her in a vulnerable position vis‐à‐vis you. It is critically important that you should
build a sense of trust in the fi rst few minutes so that the interviewee can comfort-
ably open up to you. Use thoughtful and non‐judgmental language. Sometimes, it
will be necessary to avoid using certain words that may aff ect your interviewees.
For example, you may avoid telling your interviewees that this research is about low
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122 Student Research and Report Writing
self‐esteem or drug abuse, which may make some interviewees uncomfortable or
feel defensive. In such cases, you may use more neutral terms. For example, you
may use “substance use” instead of “substance abuse.” On the other hand, do not
tell them anything that is untrue. Your use of the data collected from your inter-
viewees must avoid hurting your interviewees, and you must tell them the pur-
pose of your research and how you are going to use the data before the interview
begins.
Second, interviewees may be nervous because, after all, you are a stranger, and
they do not know what you will ask them. Creating a comfortable and relaxed
atmosphere for the conversation will enable your interviewee to remember bet-
ter and to feel more inclined to give honest and more detailed stories, which will
enhance the richness of the data collected. Something as simple and mundane
as dressing appropriately can aff ect the interaction. If you dress too formally or
too casually, it could become a cultural barrier to your conversation with your
interviewees. You should try to talk in a similar style as your interviewee, using a
level of vocabulary with which your interviewee will feel comfortable. If an inter-
viewee feels either inferior or superior to you, the conversation may be aff ected
and become unproductive.
Ask questions in a conversational style. If you read questions as if you are read-
ing from a book, you may sound like you are testing your respondent rather than
inviting him/her to talk. Practice your questions before you meet your interviewee
so that you sound personable and welcoming in your questions. Also, since inter-
viewees often ask for clarifi cation, you should be able to explain the questions in
diff erent ways. During the entire interview, you should maintain a genuine inter-
est in the interviewee’s stories. A good listener is the best encouragement for the
story‐teller. Keep in mind that your level of engagement with the interviewee’s
story will aff ect how much information he/she will be willing to volunteer and
share with you. Smiling, nodding, short words of agreement, encouragement to
go on, and taking notes are all good gestures to show your support and interest.
Be careful about any subtle expressions or gestures you may give off , and make
every eff ort to avoid value judgments. You should eliminate any personal biases
before and during the interview; for example, you should not assume that if your
interviewee is from a poor neighborhood he/she will be unhappy or have low self‐
esteem. Likewise, if you say, for instance, “how do you feel about wearing hijab
since it is a symbol of Muslim women’s subordination?” then your interviewee
may feel that you are biased against her religion.
Third, your interview schedule may include questions regarding sensitive issues
or traumatic experiences. In this case, we suggest you start the interview with
more general and not‐too‐personal questions and move onto the sensitive ques-
tions later once a rapport is established. For example, if your research is related
to marriage, you may fi rst ask your respondents general questions: when they got
married, where and how they were married, and what their general perspectives
on marriage are. Then move on to ask about problems and diffi culties with their
marriages.
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Steps of Quantitative and Qualitative Research Designs 123
Fourth, remember that the main purpose of your interviews is to gather infor-
mation, and your primary task is to listen. Keep your talk to a minimum. Say
enough to maintain a comfortable conversational atmosphere but you should
not talk more than your interviewee. In addition, be careful not to make state-
ments or ask questions that will “lead” your interviewees. What you want to get
from your interviewees is accurate information, not something you like or with
which you agree. People tend to agree, rather than to disagree, with the partners
of interactions. If your interviewee picks up clues about your own thoughts on the
topic discussed, it could infl uence his/her responses. For example, during an inter-
view about a national health insurance system, let’s suppose you asked, “Given
the problems with the long‐wait and overcrowding of hospitals, what are your
thoughts about this country’s national healthcare system?” Because the way you
mentioned those negative issues as given, the respondent will lean toward saying
negative things. If this happens, the answers you obtain are not necessarily the
respondent’s genuine thoughts.
Interviews essentially involve building a partnership with your respondent.
Thus, the cultural expectations for respectful and professional social interac-
tions should generally apply. In addition, since your goal is to obtain truthful and
valid information, you should make continuous eff orts to make the interactional
dynamics conducive for a focused and productive conversation. But bear in mind
that, ultimately, safeguarding your participant from any emotional or physical risks
should be a prevailing priority in in‐depth interviews.
What Other Qualitative Data Collection Methods Can
You Consider?
So far, we have focused on the techniques and processes used in interviews, which
are widely used data collection methods in qualitative research in many social sci-
ence disciplines. There are several other common qualitative data collection meth-
ods that are useful for other research purposes. Participant observation and eth-
nography are widely practiced data collection methods in fi elds of study such as
anthropology and cultural geography. These methods require careful planning and
execution. The guidelines for fi eld research are extensively covered in many disci-
pline‐specifi c methods books. We recommend you to look at some of the books
listed in the Reference and Further Reading section of this chapter for detailed
guidelines for these data collection methods. Here, we would like to off er a lit-
tle advice which we have not covered in the above discussions on interviews in
general.
Participant Observation and Ethnography
If you intend to conduct participant observation research, there are a few things
you need to consider. First, you should determine what your role is in the research
fi eld, whether it is in a community or in an organization. The dual role of
Wang, G. T., & Park, K. (2015). Student research and report writing : From topic selection to the complete paper. Retrieved from http://ebookcentral.proquest.com
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124 Student Research and Report Writing
researcher‐member can range from being a complete outsider researcher to being
a covert participant who completely hides his/her research role from the in‐group
members. Conducting research without disclosing your research purposes can
entail diffi cult ethical dilemmas. Since you are a student, we recommend that you
let your identity as a researcher be known and conduct your observation research
either as an outsider researcher, or as researcher‐member. This is a safer option
with fewer ethical complications for student researchers.
Even when your researcher role is disclosed, ethical dilemmas are still possi-
ble, especially when you are invited to participate in group members’ routine
activities that are against your own cultural norms or involve risky behaviors
(e.g., binge drinking, extreme hazing, spying on someone else’s privacy). Some-
times, you may use your status as a researcher to excuse yourself from having
to participate in activities that will present an ethical dilemma for you. But such
episodes may also highlight your outsider status to the group members, which
may aff ect what they are willing to share with you in the future. You should
understand that the line between a participant and an observer is a precarious
one and be aware of the ethical and practical problems that may emerge as a
result of your dual role.
Second, you will need to set aside a regular time and even space to take notes
of what you have observed, refl ect on them, and record your refl ections. After
all, you will not achieve your data collection goals if you do not set aside time for
data‐recording and evaluation. You should have a notebook or an electronic device
to take notes and record your observations and thoughts basically round the clock,
for you never know what you may encounter or when. Most often, you will take
many short notes on the spot, as jotting down a few things is all you can do at the
moment. You need to make sure that you revisit these shorthand notes (typically
during a break or at the end of the day) and write them up into more detailed
“fi eld notes.” Field notes should be detailed and including your own refl ections,
interpretations of things observed, and even direct quotes from conversations you
had with an informant.
Third, as a researcher you will typically develop close relationships with a few
people (“key informants”) who are willing to share access to insider knowledge and
help you make connections within the community. Along with leaders of the com-
munity/site you are studying you will want to conduct in‐depth interviews with
your key informants. In general, you should not interfere with naturally occurring
interactions within the community and follow the cultural norms and expectations
in interactions with others. Try to avoid sensitive questions or questions that will
provoke group members; and you should only ask these questions of key inform-
ants with whom you have developed a trusting relationship.
Focus Groups
In essence, focus group research is grounded on the principles of interview
techniques, except that you are asking questions to a group of individuals,
instead of to one interviewee. If you plan to conduct a focus group discussion,
Wang, G. T., & Park, K. (2015). Student research and report writing : From topic selection to the complete paper. Retrieved from http://ebookcentral.proquest.com
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Steps of Quantitative and Qualitative Research Designs 125
you need to prepare an interview guide similar to one used in interview
research. Just as in interview, you should manage focus group discussions with
flexibility.
Content Analysis of Image Data
All of the data gathering techniques discussed in this section are methods for
collecting text‐based data. Field notes and interview transcripts produce nar-
rative data the meaning of which will be interpreted and summarized during
the data analysis phase. With the increasing popularity of smartphone photos,
YouTube videos, social media, and the abundant storage capacity of digital fi les,
images are becoming a routine part of the social world today. As more people
use images to chronicle personal life, maintain relationships, and record historic
moments, you may fi nd images to be good data sources to address research
questions about people’s attitudes, cognitive process, and experiences in con-
temporary society.
Images have been used as data in content analyses of magazine advertisements,
fi lms, news clips, TV shows, and other mass media. But explore other types of
image data: Instagram photos, cartoons, video diaries, and drawings. You will
undoubtedly fi nd symbolic meanings embedded in images. Your task is to interpret
the social patterns and human conditions of contemporary society (e.g., an anal-
ysis of gender stereotypes in magazine advertisements, an analysis of emotional
state using Instagram photos). Or ask participants to use images to express their
thoughts and attitudes (e.g., use drawings for an analysis of personality traits).
While the potential is great, there are not too many models of image‐based studies
in the social sciences. If you are interested in using images as your data, you will
need to think creatively about how to use them to address your research questions.
The above list is a far from exhaustive list of data collection methods. Which
method is most appropriate for your research depends on the objective of your
study, the nature and scope of your research questions, and the feasibility of the
method.
References
Babbie , Earl . 2013 . The Practice of Social Research . 13th
ed. Boston, MA : Cengage Learning .
Bogdewie , Stephan P. 1999 . “ Participant Observa-
tion .” Pp. 33 – 45 in Doing Qualitative Research . 2nd
ed., edited by Benjamin F. Crabtree and William
L. Miller . Thousand Oaks, CA : Sage .
George , Darren and Paul Mallery . 2000 . SPSS for Win-
dows: Step by Step, a Simple Guide and Reference . Bos-
ton, MA : Allyn and Bacon .
Graziano and Raulin , 1999 . A Process of Inquiry . 4th ed.
Boston, MA : Allyn and Bacon .
Orcher , Lawrence T . 2005 . Conducting Research: Social
and Behavioral Science Methods . Glendale, CA :
Pyrczak Publishing .
Ruane , Janet M . 2005 . Essentials of Research Meth-
ods: A Guide to Social Science Research . Oxford, UK :
Blackwell .
Schutt , Russell K . 2011 . Investigating the Social World:
the Process and Practice of Research . 7th ed. Thousand
Oaks, CA : Pine Forge Press .
Wang, G. T., & Park, K. (2015). Student research and report writing : From topic selection to the complete paper. Retrieved from http://ebookcentral.proquest.com
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126 Student Research and Report Writing
Exercises for Chapter 6
Exercises in this chapter are designed to assist you in constructing survey instru-
ments and interview schedules. Exercise 6.1 will help you to operationalize your
concepts into measurable indicators to be included on your survey. Exercise 6.2
provides you element of introduction statements for your surveys or interviews.
Exercise 6.3 will help you practice writing clear survey questions and provide
guidelines for a good survey instrument. Once you complete your survey instru-
ment, use Exercise 6.4 to evaluate the overall fl ow and organization. Exercise 6.5
provides a set of guidelines for interview questions for qualitative research
designs.
Exercise 6.1 Specifying Concepts into Measures
The example in the table below illustrates how you can fi rst clarify defi nitions of
the concept you are using and then translate the concept into observable measures
and survey questions. In the fi rst column of this table, list all variables in your
study. Give each variable a clear defi nition (i.e., conceptualization) in the second
column, and then operationalize it into a specifi c measure or question which will
appear in your survey instrument. Make a note on what level of measurement
each item is.
Internet Resources
American Association for Public Opinion Research web-
site off ers tips for quality surveys. http://www.aapor
.org/Best_Practices1.htm#.VHuHzDGG_‐s
University of Michigan Institute for Social Research
(IRS) Survey Research Center off ers an online guide
book for cross‐cultural survey research. http://ccsg
.isr.umich.edu/index.cfm
University of Vermont Writing Center provides tips
and reading lists for qualitative research. http://
www.uvm.edu/wid/writingcenter/tutor tips/
anthrointerviews.html
Further Reading
Dillman , Don A. 2000 . Mail and Internet Surveys: The
Tailored Design Method . 2nd ed. New York : John
Wiley & Sons, Inc .
Nardi , Peter M. 2006 . Doing Survey Research: A Guide
to Quantitative Methods . Boston, MA : Pearson
Educational , Inc.
Neuman , W. Lawrence . 2011 . Social Research Methods:
Qualitative and Quantitative Approaches . 7th ed. Bos-
ton, MA : Allyn and Bacon .
Patten , Mildred L. 2011 . Questionnaire Research: A Practi-
cal Guide . 3rd ed. Glendale, CA : Pyrczak Publishing .
Wang, G. T., & Park, K. (2015). Student research and report writing : From topic selection to the complete paper. Retrieved from http://ebookcentral.proquest.com
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Steps of Quantitative and Qualitative Research Designs 127
Your Concept/ Variable
Defi nition/
Conceptualization
Survey Question/Measure
(Some concepts may require
multiple measures)
Example: Income Annual household
income
Annual household income
before taxes (in US dollars):
1__Under $25,000
2__$25,001–$50,000
3__$50,001–$75,000
4__$75,001–$100,000
5__Over $100,000
Level of this measure: Ordinal
level
(Add as many rows as
needed)
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128 Student Research and Report Writing
Exercise 6.2 Questionnaire Design: Preparing an Introduction
When you design your survey or interview questionnaire, you need to include an
introductory paragraph. Introductions to surveys can function as informed con-
sent statement. The following elements should be included in your introduction:
1 Title of the survey
2 The purpose of your research
3 Statements that participation is voluntary
4 A polite request for participation and truthful answers
5 Guarantee of anonymity and/or confi dentiality
6 Guarantee of ethical use of the data
7 Time needed to complete the questionnaire
8 Your contact information for any questions or concerns
9 A sincere appreciation for the respondent’s participation
Now, let’s write these into a paragraph statement directed to your respondents.
Once you complete your draft introduction, have someone read it, and ask if the
passage includes each of the above elements and sounds personable and inviting.
Exercise 6.3 Questionnaire Design: Writing Clear Questions
A clear and well‐constructed survey questionnaire enhances the chance of valid data
collection. When you write survey questionnaires, pay attention to the following:
1 Make sure each question is clearly stated
2 Avoid using negative or double‐negative sentences
3 Avoid double‐barreled questions, or two questions in one
4 Answer choices must be mutually exclusive and exhaustive
5 Use impartial language
To become familiar with these guidelines, look at sample questions below. Each
question in the box has an error. Identify the error and rewrite the questions to cor-
rect the problem. After practicing with these sample questions, review your own
survey instrument according to the criteria above and revise them.
Self‐check Questions
Once you complete the list, review the measures using these questions.
✓ Does this table include all of the concepts/variables for my research?
✓ Do all of my concepts/variables have at least one measure/operation?
✓ Are the levels of measurement for each variable appropriate for the pur-
pose of my research?
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Steps of Quantitative and Qualitative Research Designs 129
1 Do you disagree that letter grades for college courses shouldn’t be
abolished?
___ strongly agree ___ agree ___ disagree ___ strongly disagree ___ don’t
know
Problem:
Rewrite:
2 Do you think that cigarette smoking is harmful to health and favor laws
that ban smoking in all public places?
___ strongly agree ___ agree ___ disagree ___ strongly disagree ___ don’t
know
Problem:
Rewrite:
3 How often do you attend religious services?
___ more than once a week ___ once a week ___ a couple of times a year
___ on holidays
Problem:
Rewrite:
4 What subject fi eld is your major?
___sociology ___psychology ___social science ___science ___other
Problem:
Rewrite:
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130 Student Research and Report Writing
Exercise 6.4 Questionnaire Design; Final Review of the
Questionnaire
Once you fi nish writing survey questions/measures for all of your variables it is
time to put them together and review them for fl ow. This is a checklist for review-
ing your survey instrument.
1 Does my questionnaire have an introduction that includes informed consent
statements?
2 Does my questionnaire include ALL of the variables for my research?
3 Are questions clearly written and free of grammatical errors?
4 Is there biased language that may infl uence the respondent’s answer?
5 Is the spacing of the questions adequate? Does the questionnaire look visually
professional (in case you are using print surveys)?
6 Does my questionnaire have proper transitional directions for the respondent?
(If your survey questionnaire has several parts, you should have a transitional
sentence to take your respondents from one part to another. For example, after
asking about school life, tell your respondents “Next, I would like to ask you
questions about your family life” before inquiring about family life. In this way,
you help your respondents move from one section of questions to another and
enable them to answer your questions more easily and accurately.)
7 How long will it take an average respondent to complete this questionnaire? Is
the time you ask of respondents adequate and reasonable?
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Steps of Quantitative and Qualitative Research Designs 131
Exercise 6.5 Evaluating Preparedness for Interviews
Questions for semi‐structured and structured interviews and focus group inter-
view questions can be evaluated using the following guidelines.
1 Have I prepared a proper introduction to establish a comfortable and safe
environment for the interviewees? (e.g., Explain who I am, the purpose of the
interviews or focus groups, and what I ask of the participants.)
2 Have I prepared an informed consent form for the participants?
3 Am I familiar with the culture of the participants and prepared to communi-
cate comfortably with them? Am I sure I have no biased or judgmental opin-
ions about the participants or the communities I am about to study?
4 Does my interview schedule cover all of the themes and issues relevant to the
research questions of my study?
5 Are my interview questions open‐ended? Are they phrased in such a way to
encourage detailed stories and answers?
6 Does any of the questions lead to simple yes or no answers? (If so, revise
them.)
7 Do I have probing questions?
8 Are the questions written in non‐biased language?
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132 Student Research and Report Writing
9 Does the interview schedule have a smooth fl ow? Are the transitions from one
question to the next natural?
10 Am I prepared to ask the questions in several diff erent ways in case the inter-
viewee does not understand them?
11 Do I have a functioning voice‐recording device and have an access to a tran-
scribing machine?
12 Have I obtained ethical clearance on my project?
Your Project Outcome after Chapter 6
At this point:
• You have clearly specifi ed the defi nitions of the concepts used in your
research questions and hypotheses.
• You have determined which data collection methods you will use.
• You have identifi ed the study population and determined the sampling
strategy to select participants from this population.
• You have constructed your survey questionnaire, or pre‐ and post‐test
measures.
• You have established contacts with the site or the community in which
you wish to conduct qualitative research.
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Qualitative Research
Finding answers to questions through hands-on investigation
What is it?
Answers questions about
social phenomena
Behaviors
Opinions/attitudes
Effects of important events
Differences between social groups
Used when:
You are asking questions that can be answered by others
When there is no, or little, printed info on the topic
What is it?
It is not:
Is there an afterlife?
What is the correct way to raise children?
Do dogs have souls?
It is!:
What is it?
It is not:
Is there an afterlife?
What is the correct way to raise children?
Do dogs have souls?
It is!:
How do children imagine an afterlife?
What parenting practices are commonly used by parents attending school?
Do veterinarians believe that dogs have souls?
Most importantly…
You must TALK TO PEOPLE in order to answer questions that drive qualitative research.
Research Question must have…
A group of people
AND
A question about what they do or believe
Ethical Considerations
Participation must be voluntary; you must have participants’ permission.
Ensure your participants understand your purpose, goals, and motivation for your research.
You should protect your participants’ identities
Use pseudonyms
Be conscious of your own researcher bias
Remember: You have a position on this issue, which means you are likely to have a reaction to the data you collect. Keep an eye on your own reactions by consistently asking yourself the following questions as you collect data:
What surprised me? (this question will help you think through assumptions you may have made/are making)
What intrigued me? (this question will help you reflect on how the results may impact your position)
What disturbed me? (this question will help track any challenges to your own values and beliefs)
This Photo by Unknown Author is licensed under CC BY-NC-ND
The Qualitative Research Process
Ask a question.
Do secondary research on the topic.
Use one of the four qualitative research methods to find out answers to questions your secondary research did not provide.
Construct a research plan.
GO INTO THE FIELD!
Analyze your research results.
Write a paper explaining, in an organized and interesting fashion, your research results as well as the answer to your original RQ.
Qual Research Methods
Observations
What you can see
Ask question about what people DO
DETAILED observation notes
Surveys
Interviews
Focus Groups
Qual Research Methods
Observations
Surveys
Large groups of people
Convenient
In person or online? (surveymonkey)
Closed questions
Interviews
Focus Groups
Qual Research Methods
Observations
Surveys
Interviews
“insider information”
Expert point of view
Open-ended questions
Focus Groups
Qual Research Methods
Observations
Surveys
Interviews
Focus Groups
Interviews with small groups (4-5 people)
Benefit: multiple responses in one sitting
Downside: some of your respondents may be influenced by the others
This Photo by Unknown Author is licensed under CC BY-SA-NC
Important point!
You must explain to your subjects that they are taking part in a study. You must explain your purpose and how their contributions will be conveyed. Here’s a standard script you can use:
You are invited to participate in a [method of study, such as a survey, interview, etc.] on [describe research project]. This study is being conducted by [your name], who chose this topic to explore across 10 weeks during a research-based writing class. Your participation in this [method of study] is voluntary and will be used to [describe how info will be used and what they can expect about confidentiality or being quoted so they can decide to participate or not]. It should take approximately [minutes/hours/sessions] to complete. By answering these questions, you consent to the information you provided being used in this study. Thank you for participating!
This Photo by Unknown Author is licensed under CC BY-SA
How do I organize Qual Research?
Very simply!
5 sections with headings:
Introduction/background
Methods
Results
Discussion
Conclusion
Annotated Bibliography
What is An Annotated Bibliography?
An annotated bibliography is a list of citations to books, articles, and documents. Each citation is followed by the ‘annotation,’ a brief (usually about 150 words) descriptive and evaluative paragraph.
What are the purposes of the annotated bibliography?
· To help you understand what you’ve researched while you write your essay;
· To provide additional information or background material for your reader (Choose only the information that ties into what your essay is saying.);
· To help you organize the main points of the research and pinpoint the topic sentences of your essay.
How to Begin:
1. First, know how many sources are required to write your essay.
2. Next, locate and record citations to the books or articles that contain useful information and ideas on your essay topic. You can use the Search Terms Assignment to help with this step.
3. Then, choose those works that provide a variety of perspectives on your topic.
4. Finally, reference the book, article, or document using APA style citation and begin writing your annotations.
How to Annotate:
Write two paragraphs of annotations for EACH SOURCE. The information included in each paragraph should be as follows:
Paragraph 1 (THEY SAY): completely and respectfully summarize the source. What argument is being made? What evidence is given in support of the argument? Be detailed and specific. What rhetorical strategies does the author use to make her/his argument?
Paragraph 2 (I SAY): Do you agree or disagree with the argument? What is convincing about it? What flaws exist in the author’s argument? How does the author’s argument influence you? How might you use the source in your paper?
How It Should Look:
The annotation on the back of this paper both summarizes and assesses the book in the citation. The first paragraph provides a brief summary of the author’s project in the book, covering the main points of the work. The second paragraph points out the project’s strengths and evaluates its methods and presentation. To help you in the writing process, I recommend writing a third paragraph that reflects on the source’s importance or usefulness for your essay.
· Bibliography must be in Alphabetical order by the first item in the reference citation (usually the author’s last name)
· Bibliographies must have hanging indent
· Bibliographies must be double-spaced. Instead of indenting your paragraphs, however, you should add a third space.
Annotated Bibliography
Ehrenreich, B. (2001). Nickel and dimed: On (not) getting by in America. New York: Henry Holt and Company.
In this book of nonfiction based on the journalist’s experiential research, Ehrenreich attempts to ascertain whether it is currently possible for an individual to live on a minimum-wage in America. Taking jobs as a waitress, a maid in a cleaning service, and a Walmart sales employee, the author summarizes and reflects on her work, her relationships with fellow workers, and her financial struggles in each situation.
An experienced journalist, Ehrenreich is aware of the limitations of her experiment and the ethical implications of her experiential research tactics and reflects on these issues in the text. The author is forthcoming about her methods and supplements her experiences with scholarly research on her places of employment, the economy, and the rising cost of living in America. Ehrenreich’s project is timely, descriptive, and well-researched.
Ehrenreich’s extensive research will provide solutions for the difficulties low wage workers experience, especially when it comes to finding affordable places to live. The author argues that the poor often spend more staying in motels due to high deposit costs on apartments, and this evidence could support a call for the government to offer stipends to landlords who waive security deposits for the poor. This information could easily be incorporated into the essay’s proposed solutions.
Chapter 6
Steps of Quantitative and
Qualitative Research Designs
Since the guiding principles and procedures for quantitative and qualitative
research are quite diff erent, the two kinds of research design call for somewhat dif-
ferent knowledge and skills
.
In this chapter, we are going to illustrate more detailed
steps of quantitative and qualitative research designs and some issues to consider
at each step. We will fi rst discuss steps of quantitative research designs. Qualita-
tive research designs will be discussed in the second half of the chapter. If you
have already determined that your study calls for a qualitative research design and
are only interested in qualitative research, skip directly to the section on qualitative
research design.
Quantitative research designs include various methods including surveys, exper-
iments, and content analysis. Since the most commonly used quantitative research
method is questionnaire survey, we will focus on the steps involved in survey
research and assume the discussion will help those interested in other quantitative
research methods as well. Students frequently ask us these questions when design-
ing their survey research projects:
• What are my independent variable and dependent variables?
• How do I select a sample to study from my target population?
• What is an acceptable sample size for survey research?
• How do I turn my concepts into variables in survey questionnaire?
• What are levels of measurement and why do they matter?
The fi rst half of this chapter responds to these questions and relevant issues. In
designing a survey research, the following steps are usually necessary:
Student Research and Report Writing: From Topic Selection to the Complete Paper, First Edition. Gabe T.
Wang and Keumjae Park. © and Published 2016 by John Wiley & Sons, Ltd.
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106 Student Research and Report Writing
What Are Your Independent and Dependent
Variables?
The term “independent variable” is commonly used in social sciences to refer to
the cause, or the variable that aff ects the other in a hypothesized relationship. The
term “dependent variable” refers to the eff ects or outcomes in a hypothesized
relationship. For example, let’s consider the research question, “How do relation-
ships with parents aff ect teenagers’ school performance?” Suppose you expect that
teenagers who do not have the typical quarrelsome relationships with their parents
will do better in school than those who have a lot of confl icts with their parents.
The independent and dependent variables are already implied by your research
questions. The independent variable in this example would be relationships with
parents and the dependent variable would be school performance. Similarly, if
your research question is “Are teenagers’ grades negatively aff ected by gravitation
toward social media?” then your independent variable is “gravitation toward social
media” and your dependent variable is “grades.” Since you are likely to have more
than one research questions in your study, you may have multiple independent and
dependent variables.
Sometimes, you may have several independent variables and one dependent
variable, and vice versa. For example, questions such as “Do regular medical check‐
ups, exercise, and suffi cient vegetable intakes reduce the likelihood of cancer?” and
“Do cigarette bans in public buildings and higher cigarette taxes encourage smok-
ers to quit smoking?” have multiple independent variables and a single dependent
variable. On the other hand, a research question on the academic and emotional
eff ects of bedtime reading during early childhood assumes one independent vari-
able (“bedtime reading”) and multiple dependent variables (the various “academic
and emotional eff ects”). It is a good practice to write down your research questions
and label your independent and dependent variables.
When you identify and label your independent and dependent variables, you
should be quite clear in your mind that an independent variable is the cause of the
dependent variable and a dependent variable is the eff ect of the independent varia-
ble. A dependent variable must be able to vary or be aff ected when it is infl uenced
by the independent variable. In another example, if you use education as an inde-
pendent variable and salary as a dependent variable, then, you are anticipating that
the salary of your respondents will change when their level of education changes.
If a variable cannot vary or cannot be aff ected, then it cannot be used as a depend-
ent variable. For example, someone’s race and gender cannot be changed by the
infl uence of other variables; thus, they cannot be used as dependent variables.
In the examples above, abstract concepts such as “relationship with parents,”
“gravitation toward social media,” and “emotional eff ects,” need to be more speci-
fi ed and operationalized into measurable indicators so that you can quantify them.
Operationalization is a step where you identify very specifi c indicators or measures
for your concepts. For example, “relationships with parents” are not something
you can directly observe, but you can use some very specifi c indicators for a good
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Steps of Quantitative and Qualitative Research Designs 107
or a bad relationship. Quantifi able indicators, such as “number of arguments a
teenager had with his/her parents within a month,” “number of times a teenager
received a punishment from parents within a month,” and “number of times a
teenager violated rules set by parents” are all good ways to measure whether a
teen has a good relationship with his/her parents. Or, you can simply ask the teen
respondents to rate the quality of their relationship with parents on a scale of one
to ten. The concept, “gravitation toward social media” is hard to measure itself.
You will need to use tangible measures such as “time spent on social media each
night.” Likewise, the concept “emotional eff ects” can be specifi ed into multiple
questions gauging how happy the child is, how social the child is, or how energetic
and curious the child is, and so on.
How Do You Select a Sample to Study from Your
Target Population?
What group of people or cases is your research about? Do your research questions
concern the general population, a particular group of people, countries, schools,
or other social organizations? The answer to these questions will be your study
population, or target population; the term refers to the group of people or cases
about whom you will conduct your study and to whom you will apply the fi ndings
of your study (Babbie 2013 ). Your population is also the pool of cases from which
you will select a sample, or a subgroup of the cases you will actually study. As you
can imagine, if you select a sample that resembles your population closely, you will
be able to use your fi ndings to tell something about your study population. But
if your sample does not resemble your study population, your ability to use your
study fi ndings to predict the patterns in the study population is limited. Suppose
you have selected a group of students from your university whose average grade is
an A. You know it is unlikely that this sample will refl ect what the average grade is
in your university. The extent to which your sample “looks like” your study popu-
lation is called “ representativeness ”; study fi ndings from a representative sample
can be generalized to the study population. For instance, if a group of spectators
selected by random drawing of numbers happens to have the same demographic
characteristics as the spectators in the entire stadium, this sample will be repre-
sentative of the crowd in the stadium. This means that, if there is more rooting
for Team A in this sample, you can generalize that there will be more support for
Team A among the entire stadium crowd.
How do we select a representative sample? Social science methods teach us that
we can approximate a representative sample by reducing systemic selection biases
in sample drawing process. In general, a selection method which only relies on
random chance is considered as having no systemic selection biases (Babbie 2013 ).
There are a variety of diff erent ways to draw a sample from the study population:
simple random sampling, systematic random sampling, stratifi ed random sam-
pling, cluster sampling, quota sampling, snowball sampling, purposive sampling,
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108 Student Research and Report Writing
and availability sampling. Some of these sampling techniques select participants
using random drawing while others do not. The specifi c steps and details of diff er-
ent sampling strategies are beyond the scope of this book. If you need to refresh
your memory on how to draw a particular type of sample, consult some of the
references listed in this chapter.
In the broadest sense, sampling methods fall within two groups: probability and
non‐probably sampling. In the above list, all the variety of random sampling and
cluster sampling fall in the probability sampling category. Quota sampling, snow-
ball sampling, purposive sampling and availability sampling are non‐probability
sampling. Probability sampling methods select participants based only on random
chance. Sampling theory considers this as the best way to obtain a representative
sample . To use probability sampling methods, you need access to the sampling
frame , which refers to the roster of all units in your study population (e.g., an
approximate list of citizens of a country, a list of residents of a community, a list of
all schools, organizations, student roster, and so on), so that everyone is in the pool
of available subjects and only random chance can determine whether someone is
selected to be included in the sample.
Non‐probability samples are used when researchers do not have access to the
sampling frame, or do not have a clearly identifi able study population (such as
undocumented immigrants, homeless population). Research done by students
like you often has to resort to non‐probability sampling methods simply due to
insuffi cient time and resources. Non‐probability sampling methods are likely to
introduce sampling biases because factors other than random chance will aff ect the
selection process. It is okay to study a non‐probability sample, especially for a small
scale exploratory study, or if you are conducting qualitative research. Just keep in
mind that your fi ndings will have limited generalizability, and the limitation should
be included in the discussion of your fi ndings.
When you select your study population, make sure that you can gain access to
them. If your study population is minors (such as children or juveniles) or people
with limited power (such as prisoners), you may face particular diffi culties obtain-
ing informed consents from guardians or getting permission from the heads of
the institutions to enter the sites to collect data for your research. Therefore, think
carefully about access before you decide to study a particular population.
After you decide on your study population, decide on what your unit of analysis
is. It may be individuals, universities, organizations, or countries, depending on
what is most appropriate for your research.
What Is an Acceptable Sample Size for Surveys?
Another issue to consider is sample size. Regardless of whether you use probability
sampling or non‐probability sampling, the size of the sample is an independent
issue which requires your attention. If you want to conduct surveys and use com-
puter software to do data analysis, you need a suffi cient number of respondents in
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Steps of Quantitative and Qualitative Research Designs 109
your sample. As a general guideline, a minimum of 400 cases will be amenable for
statistical data analysis. This suggestion is to reduce sampling error due to sample
size. If you have a sample size of 400 cases, the standard sampling error will always
be 5% or smaller no matter what the variation is in the study population (Babbie
2013 ). In reality, however, it may be unrealistic for a student researcher to be able to
draw a suffi cient size sample; you are more likely to work with much smaller size
sample due to the time and resource constraints. In deciding your sample size, con-
sult with your project supervisor, or professor, as they may have specifi c guidelines
or requirements for sample size. Generally speaking, three principles are useful in
determining sample size.
First, the larger the sample size, the smaller your standard sampling error will
be. At the same time, your sample is more likely to resemble the characteristics
of your population and you will be able to generalize your fi ndings to the target
population.
Second, if you are conducting a quantitative study, most statistical analysis tech-
niques used in social sciences assume a normal, or the bell‐curve distribution of
data. If the sample is too small, say less than 100 cases, there is a good chance
that you will not meet this assumption of a normal distribution. Our advice is
to obtain at least a sample size of 100 respondents, if you plan to use statistical
analysis techniques. With that number you will be able to use commonly used
descriptive and inferential statistical techniques such as cross‐tabulation analyses,
chi‐square tests, t‐tests for comparison of means, and so on. Keep in mind that you
may receive invalid answers, which you will exclude from your analysis; to obtain a
sample size of a 100 valid cases; you may need to go slightly beyond your targeted
sample size when you collect the data.
Third, a small sample size may produce insignifi cant statistical results simply as
a function of the sample size. Sometimes, small samples require you to use special
statistical measures other than the commonly used measures mentioned above.
According to probability theory, when sample size decreases, the standard error
increases. For example, if you do a chi‐square test with a very small sample, you
may fi nd that many of the cells in your cross-tabulation have fewer than fi ve cases
and your chi‐square value is not statistically signifi cant. If you have more than 25%
of the cells with fewer than fi ve counts, your chi‐square analysis is not acceptable
(George and Mallery 2000 ). If this is the case, you cannot use chi‐squire analysis to
test whether two variables are statistically independent of each other. On the other
hand, if you only intend to use simpler descriptive statistics such as percentages
and graphs to answer your research questions, a sample size smaller than 100 can
still work.
A “robust” sample, or a suffi ciently large and representative sample, is needed
if your goal is to perform an explanatory study. This is one reason why many
researchers turn to secondary data, data collected by governments or large organ-
izations with resources, for explanatory studies. As a student researcher, you are
likely to conduct a study on a modest size sample using non‐probability sampling
strategy. This is quite all right, as long as you are mindful of the limitations of
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110 Student Research and Report Writing
your methods, make cautious interpretation of your fi ndings, and discuss the limi-
tations in your report. If you and your supervisor decide to work with very small
sized sample, say less than 50 respondents, you may consider it as a pilot test for a
full‐scale study at a later time.
A pilot study can yield valuable information; you can get a sense of how survey
questions are interpreted by the respondents, gauge whether the length is appro-
priate, estimate what response rates you can anticipate, and detect any potential
problems in administering the surveys. These are important learning experiences
and skills‐building processes which make your project meaningful. You will also
obtain some descriptive statistics about the patterns of behaviors and attitudes you
are investigating with a pilot study. If you must resort to very small sample sizes
due to the constraints in time and resources, consult your project supervisor about
designing a well‐planned pilot study which will give you a good foundation for
future projects; this can be more meaningful assignment than a poorly conducted
research on a larger sample.
Also, the above guidelines are for quantitative studies requiring statistical data
analyses. Qualitative research is most often done on a sample much smaller than
100. Sampling issues in qualitative research will be further discussed later in the
chapter when we review steps of qualitative research.
How Do You Turn Your Concepts into Variables in
Surveys?
If you conduct a survey, develop your survey questionnaire to include questions on
the independent and dependent variables and demographic information needed
for your research. Most surveys include demographic questions to collect basic
information about your study population. Typically, you ask about gender, age,
level of education, and socioeconomic status, and race and ethnicity. However,
which information is needed depends on your research questions. For example,
if you study university students’ adjustments to campus life away from home, it
may be relevant to ask how many years they have attended university. Before you
start to construct your survey questions, you should have a clearly identifi ed list of
your independent, dependent, and demographic variables. If you have an abstract
concept in your research question, you need to operationalize it as we described
earlier in this chapter. In surveys, most concepts are measured by one or a set of
questions. For example, if your research question is “How do gender, race and age
aff ect student school performance,” then you will need to specify and measure
school performance. In this case, you cannot directly ask your respondents what
their school performances are. Instead, you need to identify indicators such as stu-
dent Grade Point Average, class attendance, or time spent on study to measure
school performances.
Since you need to include measures for all your concepts, you should have at
least one question for each of your concepts in your survey instrument. Some
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Steps of Quantitative and Qualitative Research Designs 111
simple variables such as gender, age, whether or not one is in university, whether
or not one supports a political candidate can be suffi ciently measured by a single
question item. But for abstract concepts, or concepts that have a broader range of
meaning you may need more than one indicator. For example, if your variable is
depression, a composite question consisting of several items asking about diff erent
symptoms of depression may be a far better measure than a single question asking
whether one experiences depression. The key idea is that the indicators in your
survey instrument should capture the full meaning of your concepts.
In the U.S., students in university classes participate in teaching evaluation sur-
veys at the end of the semester. The purpose of the surveys is obviously to measure
“good teaching.” Imagine that schools use just one question, “does your professor
demonstrate good teaching?” This single item indicator would be not only too
simplistic, but also vague as each student may have diff erent ideas about what good
teaching is. Typical teaching evaluation surveys thus include several questions on
various aspects of teaching, such as knowledgeability, organized presentation, use
of examples, interaction with students and so on.
In developing survey questions, please pay attention to the validity of your
questions to make sure that you will get what you intended to get, or measure
what you mean to measure. For example, student GPA is a good indicator of
academic performance. How many times students visit the library may not be a
direct measure of academic performance. It may aff ect academic performance, but
library use itself is not a valid measure of academic performance. Your measures
should also have reliability which refers to the quality of a measure that produces
the same value or observation repeatedly. For example, reading a thermometer is a
much more reliable measure for fever than feeling the temperature with your hand
on someone’s forehead, since the judgment from touching one’s forehead can be
capricious. Likewise, asking “how many times did you drink last week?” is more
reliable measure than asking “do you drink often?”
What Are Levels of Measurement and Why Do They
Matter?
When you design your survey questions, think about what kind of data analysis
you are going to conduct after you collect your data. Statistical data analysis pro-
cedures are often closely related to the mathematical property of your variables,
called “ the levels of measurement .” Given that statistical analyses are based on
mathematical computations of various statistics, the mathematical qualities of the
numerical data you collect will infl uence data analysis.
Let us use a simple example. For the demographic variable, gender, researchers
often give the numeric code “1” for male respondents and code “2” for female
respondents. Computing what percentage of your sample is men or women would
be an appropriate thing to do, but computing the mean score for this variable is
not, as there is no such thing called “average gender.” You would not want to use
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112 Student Research and Report Writing
statistical technique “mean score” when you perform an analysis of variables such
as religion, nationality, or favorite Thai dish. This is because all of these examples
are variables with no mathematical meaning, or no variations in amount. On the
other extreme, if you ask respondents to write down their age, or their salaries in
precise currency terms, then you know that the diff erent values you will obtain can
be compared in quantity; computation of mean scores (e.g., average age, average
income) makes perfect sense with these variables.
These examples illustrate the diff erent mathematical qualities of the numeric
data you will obtain from diff erent types of variables. As we mentioned above,
statistical tests assume that the variables entered into the computation have certain
type of mathematical property. The types of mathematical properties are expressed
in levels of measurement. In statistical analysis, four levels of measurement are
diff erentiated , depending on the level of mathematical precision of the meas-
ure: they are nominal, ordinal, interval and ratio levels. A nominal level of meas-
urement has the lowest level of mathematical quality, because its values are num-
ber codes that do not represent actual quantities, but simply diff erent qualities. A
ratio level of measurement on the other hand is the highest level of mathematical
precision because its values actually correspond to mathematical quantities, just
like natural numbers. Let us explain each of these with some examples.
Nominal level measurements pertain to those variables whose attributes are
simply diff erent in type, not in quantity, from one another. In other words, the
numeric codes assigned to the respondents’ diff erent answers for a nominal level
measure have no implied order or quantity. The diff erence between categories
of a nominal scale is a qualitative diff erence (Graziano and Raulin 1999 ). For
example, gender, race, occupation, and marital status are usually measured at a
nominal level, as diff erent attributes respondents can take in these variables are
diff erent kinds, and not diff erent amount. Here is an example of nominal level of
measurement:
Question: Which of the following racial groups do you identify yourself with?
1 ___African American
2 ___Asian American
3 ___Caucasian American
4 ___Native American
5 ___Hispanic American
6 ___Other
In this case, the number 1 is arbitrarily used to represent the African Ameri-
cans and the number 3 is used to indicate the Caucasian Americans. The numbers
assigned to each category, however, do not mean any higher or lower social value
relative to one another. In addition, the attributes of a nominal level variable are
mutually exclusive (i.e., no overlap between attributes) and exhaustive (i.e., they
cover the full range of possible attributes for all respondents).
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Steps of Quantitative and Qualitative Research Designs 113
Ordinal level measurements assess a variable in its order of magnitude. They
have the property of magnitude (Graziano and Raulin 1999 ), or a rank‐order. In
other words, variables that are measured at the ordinal level have categorical attrib-
utes like those of nominal level measures, but they also have the advantage that
the categories can be ordered or ranked from low to high or from one extreme to
the other. Thus, the numbers assigned to diff erent attributes, or categories signify
diff erent point in a system of order. For example, social class, people’s attitudes, or
students’ academic standing are usually measured at the ordinal level. For example:
Question: Which social class do you belong to?
1 ___Lower class
2 ___Lower middle class
3 ___Middle class
4 ___Upper middle class
5 ___Upper class
6 ___Other
In this example, the respondents’ social class is rank‐ordered from what are
socially deemed lower socioeconomic statuses to higher ones. The numeric codes
do not mean exact quantities, but they indicate a rank‐order between categories
and identities. The diff erence between lower class and lower middle class is not the
same as the diff erence between upper middle class and upper class. Nor, the lower
middle class (Numeric value “2”) is exactly half as rich as the upper middle class
(Numeric value “4”). In order to make it exhaustive, “other” has been provided for
those respondents who are not sure about their socioeconomic status or who do
not want to reveal their socioeconomic status.
The interval level of measurement measures a variable’s attributes that are not
only rank‐ordered but also are separated by a uniform distance between them. Inter-
val level variables are measured in terms of a standard unit of measurement, and
therefore the categories of an interval measurement have equal intervals between
the categories. For example, temperature can be measured on an interval level.
Question: What is the temperature in your city today? ______ C°
In this example, the measurement indicates an equal diff erence between any two
units of the measurement. That is, the diff erence between 18C° and 19C° is the same
as the diff erence between 10C° and 11C°. But notice that there is no absolute zero, or
absence of a value in this scale. A temperature of 0C° is still a temperature indicating
a relative location in this scale, and it does not mean that there is no temperature.
Ratio level of measurement is the same as interval level of measurement, except
that ratio level of measurement is also based on a true zero point or an absence of
the quantity. In other words, variables that are measured on a ratio scale have the
characteristics of an interval scale plus a real zero. For example:
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114 Student Research and Report Writing
Question: How much money do you have in your pocket now? ________ Euros
In this example, if a respondent answered zero, he/she has absolutely no money
at all in his/her pocket. Likewise, if someone answered zero, for a question about
years of schooling, you can interpret this as a lack of any formal schooling.
In social science research, you will fi nd very few, if any, true interval level varia-
bles; most measures are ratio level measures. Also, the distinction between interval
level and ratio level measures does not make a diff erence in statistical computa-
tions for social sciences. Therefore, you may treat interval and ratio level of meas-
urement as if they are the same level. This is a common practice and the reason
why many people lump the two together as “interval‐ratio” level measures. If you
use software packages for statistical analysis such as IBM’s SPSS program, you will
notice that there is no option for designating a variable as an interval level meas-
urement. Instead, interval and ratio levels of measurement are both indicated with
the term “scale.”
Since the levels of measure will enable or limit the statistical procedures you
can apply at a later stage, it is important to understand diff erent levels of meas-
urement at this stage and operationalize your variables into adequate levels of
measurement. For instance, you can use bar charts or pie charts, frequencies and
percentages, and cross tabulations to analyze your data when your independent
and dependent variables are nominal level of measurement. If you want to calcu-
late means, correlations, or multiple regressions, however, your independent and
dependent variables need to be interval or ratio level measures. Similarly, to use
means or variances to compare two groups (t‐test) or several groups (one‐way
analysis of variance), your dependent variables should be measured at the interval
or ratio level. All of these procedures are explained in Chapter 9 .
If you are not sure about what kind of data analysis you will eventually use
at the time you design your survey questionnaire, we advise you measure your
independent and dependent variables at the interval or ratio level whenever poss-
ible. Of course, some variables, such as gender, ethnicity, and religious affi liation,
are naturally nominal level and cannot be measured as ratio level variables. But
there are many variables that can be measured on an ordinal scale or a ratio scale;
for example, salaries from work can be measured as an ordinal level variable
(“Which of the following range does your monthly salary belong to?”) or as a
ratio level variable (“Please write down the exact amount of your monthly salary
in U.S. dollars.”). In this case, we recommend that you use a ratio level variable.
We suggest this because statistical software packages allow you to transform ratio
level variables into a lower level measurement after the data are entered, but you
cannot do the reverse.
For example, if you collected your income data in real dollar amount (i.e., a
ratio level measure) but later decide to use simple cross‐tabulation technique for
analysis, you can recode the income variable into a new four‐ or fi ve‐category
income range variable (i.e. an ordinal level measure). No information is lost in
this recoding process, and you can still keep the data on your original variable (the
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Steps of Quantitative and Qualitative Research Designs 115
exact dollar amount for each respondent) as a separate variable. If your original
variable was an ordinal level income range variable, however, there is no way you
can estimate what exact dollar amount each respondent earned. The lesson here
is that mathematically higher level measures tend to contain more minute infor-
mation. Therefore, you should measure at the highest level possible because you
may need information with more precision at the data analysis stage.
Similarly, it is a good idea to use ordinal level measures when you have an
option to choose either nominal level or ordinal level. For example, students often
construct questions with simple “yes” or “no” answer choices. You may ask yes/
no questions but after collecting data, realize that the procedures you can use to
analyze data are quite limited. Yes/no questions contain far less information than
asking, for instance, about the frequency of particular behaviors or the degree of
support on specifi c issues. To illustrate this point, let’s compare how the following
two questions treat the same variable. When asking about college student drug
use on campus, student researchers have a tendency to ask:
Did you use marijuana on campus last month?
1___Yes 2____No
But rephrasing this into an ordinal level question off ers more precise informa-
tion on each respondent and allows a greater range of statistical analysis proce-
dures later on:
How often did you use marijuana on campus last month?
1 ___ Never
2 ___ Seldom
3 ___ Sometimes
4 ___ Often
5 ___ Always
6 ___ I do not know
You can use even more computer data analysis procedures, if you measure stu-
dent drug use on a ratio level:
How many times did you use marijuana on campus last month? _______
The lesson is that, in designing your questionnaire, consider what kind of data
analysis you will conduct and determine the appropriate level of measurement for
each of your variables. Quantitative research designs are suitable to pursue relatively
straightforward questions and clearly defi ned hypotheses. If your research questions
call for the in‐depth understanding of a complex problem, a deeper examination of
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116 Student Research and Report Writing
a particular experience, or comprehensive knowledge of a group of people, you
will be better off collecting detailed qualitative data for your empirical research. We
now discuss some methodological issues relevant to qualitative research.
There are more issues related to survey designs which we have not covered in this
chapter. Some are beyond the scope of this book. We hope that you will fi nd more
in‐depth details from your research methods textbooks. One issue we have not dis-
cussed in this chapter is how to write clear and eff ective questions. There are many
tips for properly wording survey questions. Essentials of Research Methods: A Guide to
Social Science Research (Ruane 2005 ) has two excellent chapters on this issue.
What Do You Need to Know about Qualitative
Research Designs?
The main benefi t of qualitative data collection methods is two‐fold: 1) they allow
you to obtain data refl ecting the participants’ own perspectives, and 2) they are
conducive for a wide range of observations without a pre‐conceived notion about
the issue or the situation. These methods are designed to have participants’ volun-
tary information in their own words, or let you make observations in the natural
setting of a situation. There are various ways of conducting qualitative research,
and each way may be appropriate for a specifi c type of research project. As we
discussed in Chapter 5 , some of the popular qualitative data collection strategies
include in‐depth interviews, participant observations, ethnography, and focus
groups. Since in‐depth interviews are most frequently used by students, we will
focus on the steps and concerns involved in in‐depth interviews. When designing
their qualitative research project, students frequently ask us these questions:
• How do I construct my interview questions?
• How do I select people for interviews?
• What should I do during the interviews?
• What other qualitative data collection methods can I consider?
The remaining part of this chapter responds to these questions and relevant
issues. In designing in‐depth interviews, the following steps are usually necessary:
How Do You Construct Your Interview Questions?
In‐depth interviews are based on open‐ended questions that reject pre‐conceived
predictions and allow participants to express themselves in their own words. They
off er you a better sense of what respondents feel and think. For example, suppose
you are interested in understanding the challenges new immigrants face in adjust-
ing to their host country, your research question may ask: What are the main prob-
lems new immigrants and refugees face in integrating into their receiving country?
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Steps of Quantitative and Qualitative Research Designs 117
This question is broad and open‐ended. You have no independent or dependent
variables, nor is there a need to identify them.
But you are not approaching this question blindly, either. Having reviewed exist-
ing literature, you probably now know something about issues that concern new
immigrants. For example, you know that the literature on immigrants and refu-
gees discusses legal barriers for work permits and citizenship, cultural diff erences,
language barriers, prejudice and discrimination based on race, ethnicity and reli-
gion, and economic diffi culties. With this knowledge you can identify focal areas in
designing your interview research and constructing interview questions.
In‐depth interviews are not usually highly structured and allow you to spontane-
ously explore emerging new issues and themes as the interview unfolds. To collect
relevant information consistently across many interviewees, however, you need
to have some standard set of broad questions you want to ask everyone. This set
of questions is called an “interview schedule.” Depending on how many specifi c
questions you have in your interview schedule, and how closely you plan to adhere
to the prepared list, you may have highly structured interviews, semi‐structured
interviews, or unstructured interviews. Which one you choose to do depends on
the goals of your study. Here are a few tips for constructing an interview schedule.
First, keep the questions open‐ended and allow your respondents to answer
them in their own way. Questions leading to a yes/no answer are not productive
in interviews. For example, if you ask, “Did you have a lot of diffi culty fi nding jobs
after you came to this country?” your interviewee is likely to simply answer, “Yes, I
did” and stop. Instead, if you ask, “Tell me about how you tried to fi nd work when
you arrived here, and how you found your fi rst job,” you are inviting your respon-
dent to tell you about his experiences and diffi culties with his job search.
Some immigrants may not have had much trouble fi nding a job because a rel-
ative had a job off er already lined up while others lacking connections may have
had a long period of unemployment and economic hardship. Respondents may
voluntarily tell you stories about demanding and unrewarding work or share their
experiences of discrimination while telling their job search stories. You will be in
a better position to collect these varying accounts on work when you use open‐
ended phrases like the second example. Moreover, you will be able to assess what
issues matter most about work and job opportunities by paying attention to the
issues your interviewees mention fi rst. These rich details will not surface, if you
ask “yes/no” questions.
Second, for each interview question, you should think about “probing ques-
tions,” or follow‐up questions for more details or information on a closely related
issue. Probing is a critical skill in interviews since your respondents are unlikely to
pour out details about their experiences right from the beginning. In our exam-
ple about the job experience, your interviewee may give you a relatively short
answer, for instance, “I have a friend who worked in a restaurant. He told me that
his manager may give me a job. So, I went with him one day and started to work
there.” At this point, probe further in a few diff erent directions. If your research
focus is job market conditions and opportunities for immigrants, ask follow‐up
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118 Student Research and Report Writing
questions about how long he searched before getting his fi rst job, what he did
to search for work, what types of job were available, and so on. If your research
question concerns the role of social networks for new immigrants, which many
scholars consider important, then probe more about this “friend” who helped your
interviewee. Ask: “Who was that friend? Was he someone from your country? Did
you meet him here?” These questions will help your respondent elaborate on his
relationship with his friend and possibly other resources and information that may
be vital to his settlement into a new community.
Third, keep your interview schedule fl exible enough so that you can pursue new
leads or switch the order of your questions as the conversation unfolds. Depending
on your research questions and style, interviews can be more or less structured.
You may feel skilled and comfortable conducting interviews only with an outline
of themes in mind (unstructured interviews). Or you may be concerned about
forgetting some important questions and would prefer to stay close to the prepared
interview schedules (structured interviews).
Most of you are likely to be somewhere in‐between and follow a style called
“semi‐structured” interviews. In semi‐structured interviews, you will prepare an
interview schedule including some probing questions but you will also let yourself
explore a diff erent theme or direction as new clues emerge in the conversation. Be
ready to embrace newly found issues in the course of the interview. In our exam-
ple of an interview an immigrant, suppose your interviewee told you that one
day he got into a fi ght with another immigrant worker from Africa because he did
not like the African song this man was singing to himself all the time. This may
prompt you to ask about possible confl icts between workers of diff erent ethno‐
religious backgrounds in places where many immigrants work. Even if you had
not planned to ask questions about this issue, this may well be a relevant focal
point in your research. Do not hesitate to add new questions, if there are newly
emerging interests as your interviews progress.
Fourth, interviews are conversations and the fl ow of the interviews may aff ect
the amount of information you obtain. In general, it helps the interviewees’
thought process if you move from one question to the next with a sense of con-
tinuity. For instance, ask about marriage and then about children; there is a close
connection between these themes and your interviewee is likely to stay focused
and remember more relevant details. But if you ask about marriage fi rst, then edu-
cational background and old school friends, and fi nally about children, it may be
diffi cult for your respondent to recall information, jumping back and forth on dif-
ferent periods of the life course. This consideration of smooth fl ow can guide you
in constructing your interview schedules as well as carrying out the interviews.
Overall, having a prepared interview schedule/guide off ers at least two advan-
tages. One is that you will have a clear direction before you start your interviews
and will be able to include all your main questions. The other is that you are likely
to collect consistent information from all interviewees. As long as you use the
interview guides with fl exibility, you will enhance your chance of obtaining richer
and more useful data.
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Steps of Quantitative and Qualitative Research Designs 119
How Do You Select People for Interviews?
Just as in quantitative research, qualitative studies require careful thoughts on
sample selection. Selecting appropriate participants for your research will have an
impact on your data, since you are likely to have a relatively small sample in an
in‐depth qualitative study. It may be unrealistic to gather a sample that is truly
representative of your study population. One way to handle this limitation is to
attempt to select a sample heterogeneous enough to capture the diversity of expe-
rience and perspective on the topic you are studying. Selecting interviewees par-
allels the process of sampling in quantitative research, except that you are highly
likely to rely on a non‐probability sampling methods. When you select participants
for an interview‐based study, carefully consider the heterogeneity of the sample, a
minimum threshold of interviewees, and the pros and cons of diff erent sampling
methods.
For the heterogeneity of your sample; you probably want to select people of
diff erent demographic attributes so that you can collect accounts from diverse
points of views. For example, if you want to know about experiences of homeless
people in a large city, include people of diverse demographic backgrounds, with
dependent families and without, from diff erent service facilities, and from diff erent
geographic sections of the city. Beginning researchers often resort to interviewing
those who are easily available. But if you select people solely in the same social
network, you may obtain similar stories; people in the same social network tend to
have common characteristics. It is convenient to recruit participants from one sup-
port group for recovering alcoholics, for instance, but this is not the best strategy
for discovering a range of patterns, experiences, and stories about this population.
To increase the heterogeneity of your sample, you may purposively reach out to
participants with diverse social characteristics (e.g., by gender, age, nationality, eth-
nicity, level of education, race, and so on). Selecting participants from multiple
geographic locations is one strategy to increase diversity and improve representa-
tiveness within your sample.
Second, even if a relatively small size sample is acceptable for qualitative stud-
ies, you need to have a minimum number of participants in your sample. If you
interview only four or fi ve individuals, you will be unable to fi nd enough common-
alities, or draw any generalizable conclusions. In this case, the outcomes become
little more than fi ve individual stories. If you are conducting a case study which
is the type of qualitative study zooming into comprehensive and in‐depth exam-
ination of a single or a few cases, then fi ve individual stories would still be good
for case‐oriented analyses. But if your objective is to summarize some theoretical
themes, then you may need a minimum number of interviews to represent a com-
mon story of your study population.
The sample size in qualitative research is usually not pre‐determined, but a
“ saturation point ” is often recommended as the threshold for concluding the
data collection. The “saturation point” is defi ned as the moment when additional
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120 Student Research and Report Writing
interviews no longer produce new information (Schutt 2011 ); in other words,
when you feel that you are getting the same story over and over, you may have
reached the saturation point. The saturation point can come earlier or later in your
interviews depending on the scope of your research questions and the heteroge-
neity of your sample.
In our experiences, published qualitative studies tend to be based on more than
30 interviews and rarely go over a sample size of 100 unless secondary data sources
were used. For semester‐long undergraduate research, we think a minimum of
13–15 interviews is realistic, although you may not be able to reach the saturation
point. If you have a year or longer to work on your research project, you will have
a better chance of getting enough interviews to reach the saturation point. We
strongly recommend that you consult with your project supervisors and faculty
mentors who are familiar with the scope of your research questions and the con-
straints of resources under which you are conducting this research. Since most
qualitative research is exploratory in nature, a non‐representative sample itself
does not disqualify the entire research. You just need to interpret its fi ndings with
caution and avoid an overgeneralization.
A few non‐probability sampling methods you may want to consider, instead of
availability sampling, include purposive sampling, quota sampling, and snowball
sampling. Purposive sampling involves selecting individuals who fi t certain crite-
ria required by your research questions. Individuals may be selected because they
belong to certain groups, demographic categories, or they are likely to have special
information or knowledge that can help your study. For example, for a study of a
city’s sanitation services, you may purposively interview people involved in sanita-
tion services including the city’s director, managers of various divisions, and street
cleaning crew. If it is important for you to include in your study various types of
business organizations, you may also set a quota in your sample for various busi-
ness categories and reach out to diff erent business sectors according to your quota.
This strategy is called “ quota sampling .” Quota sampling helps you to ensure a
representation of various groups of cases in your sample and to achieve a make‐up
of the sample that is similar to your target population. Snowball sampling is a
strategy to recruit additional participants by utilizing referrals from earlier inter-
views. Just as a snowball grows in size by rolling it, you will rely on earlier inter-
viewees to introduce new participants to you. In snowball sampling, you will ask
each interviewee to introduce you to other potential participants who meet the
criteria to be included in the study; the chain of referrals will enable you to amass
growing numbers of people into your study, like an enlarging snowball. Snow-
ball sampling is especially useful if your study population is not easily identifi able,
such as homeless people, victims of intimate partner violence, or undocumented
immigrants.
Although non‐probability sampling methods are commonly used in qualitative
research, this does not mean that you cannot use a probability sampling strategy.
If your study population is a small sized group and if you have a list of every-
one in the population, you can pursue the random sampling strategy described
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Steps of Quantitative and Qualitative Research Designs 121
earlier in this chapter. For instance, if you are conducting a case study of a busi-
ness corporation and you can obtain the list of all employees in this organization,
make a random selection using the list. But bear in mind that if the sample size
is small, random sampling does not promise any greater representativeness than
the non‐probability sampling strategies listed above. You may even use a combi-
nation of diff erent selection strategies to recruit participants for your qualitative
study. The key is to select a group of participants from which you will be able to
collect a maximum amount of information for your research questions. Thus, the
scope and goals of your research will determine the best methods for selecting
interviewees.
What Should You Do to Have Productive Interviews?
Since interview research entails face‐to‐face interactions between the interviewer
(you) and the interviewees, paying special attention to the following aspects of the
interviewing process will positively aff ect the quality of your interviews.
First, you want to have a nice introduction. A proper and friendly introduc-
tion will set the tone of the entire interview. One of the main purposes of the
introduction would be to create a comfortable connection with the interviewee.
There is another important purpose, which is to ensure ethical process. During
the introduction, you need to let the interviewee get to know you and learn about
your study, so that he/she feels comfortable in agreeing to participate. Here are
some very important things to include in your introduction: information about
yourself (e.g., your name, institutional affi liation, position/title etc.), infor-
mation about your study (e.g., the objectives, sponsorships of the study if any,
the purpose of the interview, the use of data, etc.), and information about the
recruitment of the respondent (e.g., how his/her name was found, why he/she
was selected, what makes him/her a suitable interviewee for the study, etc.), and
what you will ask of the respondent (e.g., types of question to discuss, length
of the interview). Following the research ethics protocol, you should also clarify
whether the interview will be tape‐recorded, how confi dentiality is guaranteed,
and how you will safeguard the information collected. If there is a potential emo-
tional risk for the interviewee, you must disclose it before he/she agrees to sign
the informed consent statement. Also, ask the interviewee if she has any other
questions about the study.
Openness and respect are important guiding principles in this process. Remem-
ber that the participant is doing you a huge favor by agreeing to give you his/her
time sharing personal experiences and thoughts. At the same time, this favor puts
her in a vulnerable position vis‐à‐vis you. It is critically important that you should
build a sense of trust in the fi rst few minutes so that the interviewee can comfort-
ably open up to you. Use thoughtful and non‐judgmental language. Sometimes, it
will be necessary to avoid using certain words that may aff ect your interviewees.
For example, you may avoid telling your interviewees that this research is about low
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122 Student Research and Report Writing
self‐esteem or drug abuse, which may make some interviewees uncomfortable or
feel defensive. In such cases, you may use more neutral terms. For example, you
may use “substance use” instead of “substance abuse.” On the other hand, do not
tell them anything that is untrue. Your use of the data collected from your inter-
viewees must avoid hurting your interviewees, and you must tell them the pur-
pose of your research and how you are going to use the data before the interview
begins.
Second, interviewees may be nervous because, after all, you are a stranger, and
they do not know what you will ask them. Creating a comfortable and relaxed
atmosphere for the conversation will enable your interviewee to remember bet-
ter and to feel more inclined to give honest and more detailed stories, which will
enhance the richness of the data collected. Something as simple and mundane
as dressing appropriately can aff ect the interaction. If you dress too formally or
too casually, it could become a cultural barrier to your conversation with your
interviewees. You should try to talk in a similar style as your interviewee, using a
level of vocabulary with which your interviewee will feel comfortable. If an inter-
viewee feels either inferior or superior to you, the conversation may be aff ected
and become unproductive.
Ask questions in a conversational style. If you read questions as if you are read-
ing from a book, you may sound like you are testing your respondent rather than
inviting him/her to talk. Practice your questions before you meet your interviewee
so that you sound personable and welcoming in your questions. Also, since inter-
viewees often ask for clarifi cation, you should be able to explain the questions in
diff erent ways. During the entire interview, you should maintain a genuine inter-
est in the interviewee’s stories. A good listener is the best encouragement for the
story‐teller. Keep in mind that your level of engagement with the interviewee’s
story will aff ect how much information he/she will be willing to volunteer and
share with you. Smiling, nodding, short words of agreement, encouragement to
go on, and taking notes are all good gestures to show your support and interest.
Be careful about any subtle expressions or gestures you may give off , and make
every eff ort to avoid value judgments. You should eliminate any personal biases
before and during the interview; for example, you should not assume that if your
interviewee is from a poor neighborhood he/she will be unhappy or have low self‐
esteem. Likewise, if you say, for instance, “how do you feel about wearing hijab
since it is a symbol of Muslim women’s subordination?” then your interviewee
may feel that you are biased against her religion.
Third, your interview schedule may include questions regarding sensitive issues
or traumatic experiences. In this case, we suggest you start the interview with
more general and not‐too‐personal questions and move onto the sensitive ques-
tions later once a rapport is established. For example, if your research is related
to marriage, you may fi rst ask your respondents general questions: when they got
married, where and how they were married, and what their general perspectives
on marriage are. Then move on to ask about problems and diffi culties with their
marriages.
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Steps of Quantitative and Qualitative Research Designs 123
Fourth, remember that the main purpose of your interviews is to gather infor-
mation, and your primary task is to listen. Keep your talk to a minimum. Say
enough to maintain a comfortable conversational atmosphere but you should
not talk more than your interviewee. In addition, be careful not to make state-
ments or ask questions that will “lead” your interviewees. What you want to get
from your interviewees is accurate information, not something you like or with
which you agree. People tend to agree, rather than to disagree, with the partners
of interactions. If your interviewee picks up clues about your own thoughts on the
topic discussed, it could infl uence his/her responses. For example, during an inter-
view about a national health insurance system, let’s suppose you asked, “Given
the problems with the long‐wait and overcrowding of hospitals, what are your
thoughts about this country’s national healthcare system?” Because the way you
mentioned those negative issues as given, the respondent will lean toward saying
negative things. If this happens, the answers you obtain are not necessarily the
respondent’s genuine thoughts.
Interviews essentially involve building a partnership with your respondent.
Thus, the cultural expectations for respectful and professional social interac-
tions should generally apply. In addition, since your goal is to obtain truthful and
valid information, you should make continuous eff orts to make the interactional
dynamics conducive for a focused and productive conversation. But bear in mind
that, ultimately, safeguarding your participant from any emotional or physical risks
should be a prevailing priority in in‐depth interviews.
What Other Qualitative Data Collection Methods Can
You Consider?
So far, we have focused on the techniques and processes used in interviews, which
are widely used data collection methods in qualitative research in many social sci-
ence disciplines. There are several other common qualitative data collection meth-
ods that are useful for other research purposes. Participant observation and eth-
nography are widely practiced data collection methods in fi elds of study such as
anthropology and cultural geography. These methods require careful planning and
execution. The guidelines for fi eld research are extensively covered in many disci-
pline‐specifi c methods books. We recommend you to look at some of the books
listed in the Reference and Further Reading section of this chapter for detailed
guidelines for these data collection methods. Here, we would like to off er a lit-
tle advice which we have not covered in the above discussions on interviews in
general.
Participant Observation and Ethnography
If you intend to conduct participant observation research, there are a few things
you need to consider. First, you should determine what your role is in the research
fi eld, whether it is in a community or in an organization. The dual role of
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124 Student Research and Report Writing
researcher‐member can range from being a complete outsider researcher to being
a covert participant who completely hides his/her research role from the in‐group
members. Conducting research without disclosing your research purposes can
entail diffi cult ethical dilemmas. Since you are a student, we recommend that you
let your identity as a researcher be known and conduct your observation research
either as an outsider researcher, or as researcher‐member. This is a safer option
with fewer ethical complications for student researchers.
Even when your researcher role is disclosed, ethical dilemmas are still possi-
ble, especially when you are invited to participate in group members’ routine
activities that are against your own cultural norms or involve risky behaviors
(e.g., binge drinking, extreme hazing, spying on someone else’s privacy). Some-
times, you may use your status as a researcher to excuse yourself from having
to participate in activities that will present an ethical dilemma for you. But such
episodes may also highlight your outsider status to the group members, which
may aff ect what they are willing to share with you in the future. You should
understand that the line between a participant and an observer is a precarious
one and be aware of the ethical and practical problems that may emerge as a
result of your dual role.
Second, you will need to set aside a regular time and even space to take notes
of what you have observed, refl ect on them, and record your refl ections. After
all, you will not achieve your data collection goals if you do not set aside time for
data‐recording and evaluation. You should have a notebook or an electronic device
to take notes and record your observations and thoughts basically round the clock,
for you never know what you may encounter or when. Most often, you will take
many short notes on the spot, as jotting down a few things is all you can do at the
moment. You need to make sure that you revisit these shorthand notes (typically
during a break or at the end of the day) and write them up into more detailed
“fi eld notes.” Field notes should be detailed and including your own refl ections,
interpretations of things observed, and even direct quotes from conversations you
had with an informant.
Third, as a researcher you will typically develop close relationships with a few
people (“key informants”) who are willing to share access to insider knowledge and
help you make connections within the community. Along with leaders of the com-
munity/site you are studying you will want to conduct in‐depth interviews with
your key informants. In general, you should not interfere with naturally occurring
interactions within the community and follow the cultural norms and expectations
in interactions with others. Try to avoid sensitive questions or questions that will
provoke group members; and you should only ask these questions of key inform-
ants with whom you have developed a trusting relationship.
Focus Groups
In essence, focus group research is grounded on the principles of interview
techniques, except that you are asking questions to a group of individuals,
instead of to one interviewee. If you plan to conduct a focus group discussion,
Wang, G. T., & Park, K. (2015). Student research and report writing : From topic selection to the complete paper. Retrieved from http://ebookcentral.proquest.com
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Steps of Quantitative and Qualitative Research Designs 125
you need to prepare an interview guide similar to one used in interview
research. Just as in interview, you should manage focus group discussions with
flexibility.
Content Analysis of Image Data
All of the data gathering techniques discussed in this section are methods for
collecting text‐based data. Field notes and interview transcripts produce nar-
rative data the meaning of which will be interpreted and summarized during
the data analysis phase. With the increasing popularity of smartphone photos,
YouTube videos, social media, and the abundant storage capacity of digital fi les,
images are becoming a routine part of the social world today. As more people
use images to chronicle personal life, maintain relationships, and record historic
moments, you may fi nd images to be good data sources to address research
questions about people’s attitudes, cognitive process, and experiences in con-
temporary society.
Images have been used as data in content analyses of magazine advertisements,
fi lms, news clips, TV shows, and other mass media. But explore other types of
image data: Instagram photos, cartoons, video diaries, and drawings. You will
undoubtedly fi nd symbolic meanings embedded in images. Your task is to interpret
the social patterns and human conditions of contemporary society (e.g., an anal-
ysis of gender stereotypes in magazine advertisements, an analysis of emotional
state using Instagram photos). Or ask participants to use images to express their
thoughts and attitudes (e.g., use drawings for an analysis of personality traits).
While the potential is great, there are not too many models of image‐based studies
in the social sciences. If you are interested in using images as your data, you will
need to think creatively about how to use them to address your research questions.
The above list is a far from exhaustive list of data collection methods. Which
method is most appropriate for your research depends on the objective of your
study, the nature and scope of your research questions, and the feasibility of the
method.
References
Babbie , Earl . 2013 . The Practice of Social Research . 13th
ed. Boston, MA : Cengage Learning .
Bogdewie , Stephan P. 1999 . “ Participant Observa-
tion .” Pp. 33 – 45 in Doing Qualitative Research . 2nd
ed., edited by Benjamin F. Crabtree and William
L. Miller . Thousand Oaks, CA : Sage .
George , Darren and Paul Mallery . 2000 . SPSS for Win-
dows: Step by Step, a Simple Guide and Reference . Bos-
ton, MA : Allyn and Bacon .
Graziano and Raulin , 1999 . A Process of Inquiry . 4th ed.
Boston, MA : Allyn and Bacon .
Orcher , Lawrence T . 2005 . Conducting Research: Social
and Behavioral Science Methods . Glendale, CA :
Pyrczak Publishing .
Ruane , Janet M . 2005 . Essentials of Research Meth-
ods: A Guide to Social Science Research . Oxford, UK :
Blackwell .
Schutt , Russell K . 2011 . Investigating the Social World:
the Process and Practice of Research . 7th ed. Thousand
Oaks, CA : Pine Forge Press .
Wang, G. T., & Park, K. (2015). Student research and report writing : From topic selection to the complete paper. Retrieved from http://ebookcentral.proquest.com
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126 Student Research and Report Writing
Exercises for Chapter 6
Exercises in this chapter are designed to assist you in constructing survey instru-
ments and interview schedules. Exercise 6.1 will help you to operationalize your
concepts into measurable indicators to be included on your survey. Exercise 6.2
provides you element of introduction statements for your surveys or interviews.
Exercise 6.3 will help you practice writing clear survey questions and provide
guidelines for a good survey instrument. Once you complete your survey instru-
ment, use Exercise 6.4 to evaluate the overall fl ow and organization. Exercise 6.5
provides a set of guidelines for interview questions for qualitative research
designs.
Exercise 6.1 Specifying Concepts into Measures
The example in the table below illustrates how you can fi rst clarify defi nitions of
the concept you are using and then translate the concept into observable measures
and survey questions. In the fi rst column of this table, list all variables in your
study. Give each variable a clear defi nition (i.e., conceptualization) in the second
column, and then operationalize it into a specifi c measure or question which will
appear in your survey instrument. Make a note on what level of measurement
each item is.
Internet Resources
American Association for Public Opinion Research web-
site off ers tips for quality surveys. http://www.aapor
.org/Best_Practices1.htm#.VHuHzDGG_‐s
University of Michigan Institute for Social Research
(IRS) Survey Research Center off ers an online guide
book for cross‐cultural survey research. http://ccsg
.isr.umich.edu/index.cfm
University of Vermont Writing Center provides tips
and reading lists for qualitative research. http://
www.uvm.edu/wid/writingcenter/tutor tips/
anthrointerviews.html
Further Reading
Dillman , Don A. 2000 . Mail and Internet Surveys: The
Tailored Design Method . 2nd ed. New York : John
Wiley & Sons, Inc .
Nardi , Peter M. 2006 . Doing Survey Research: A Guide
to Quantitative Methods . Boston, MA : Pearson
Educational , Inc.
Neuman , W. Lawrence . 2011 . Social Research Methods:
Qualitative and Quantitative Approaches . 7th ed. Bos-
ton, MA : Allyn and Bacon .
Patten , Mildred L. 2011 . Questionnaire Research: A Practi-
cal Guide . 3rd ed. Glendale, CA : Pyrczak Publishing .
Wang, G. T., & Park, K. (2015). Student research and report writing : From topic selection to the complete paper. Retrieved from http://ebookcentral.proquest.com
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Steps of Quantitative and Qualitative Research Designs 127
Your Concept/ Variable
Defi nition/
Conceptualization
Survey Question/Measure
(Some concepts may require
multiple measures)
Example: Income Annual household
income
Annual household income
before taxes (in US dollars):
1__Under $25,000
2__$25,001–$50,000
3__$50,001–$75,000
4__$75,001–$100,000
5__Over $100,000
Level of this measure: Ordinal
level
(Add as many rows as
needed)
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128 Student Research and Report Writing
Exercise 6.2 Questionnaire Design: Preparing an Introduction
When you design your survey or interview questionnaire, you need to include an
introductory paragraph. Introductions to surveys can function as informed con-
sent statement. The following elements should be included in your introduction:
1 Title of the survey
2 The purpose of your research
3 Statements that participation is voluntary
4 A polite request for participation and truthful answers
5 Guarantee of anonymity and/or confi dentiality
6 Guarantee of ethical use of the data
7 Time needed to complete the questionnaire
8 Your contact information for any questions or concerns
9 A sincere appreciation for the respondent’s participation
Now, let’s write these into a paragraph statement directed to your respondents.
Once you complete your draft introduction, have someone read it, and ask if the
passage includes each of the above elements and sounds personable and inviting.
Exercise 6.3 Questionnaire Design: Writing Clear Questions
A clear and well‐constructed survey questionnaire enhances the chance of valid data
collection. When you write survey questionnaires, pay attention to the following:
1 Make sure each question is clearly stated
2 Avoid using negative or double‐negative sentences
3 Avoid double‐barreled questions, or two questions in one
4 Answer choices must be mutually exclusive and exhaustive
5 Use impartial language
To become familiar with these guidelines, look at sample questions below. Each
question in the box has an error. Identify the error and rewrite the questions to cor-
rect the problem. After practicing with these sample questions, review your own
survey instrument according to the criteria above and revise them.
Self‐check Questions
Once you complete the list, review the measures using these questions.
✓ Does this table include all of the concepts/variables for my research?
✓ Do all of my concepts/variables have at least one measure/operation?
✓ Are the levels of measurement for each variable appropriate for the pur-
pose of my research?
Wang, G. T., & Park, K. (2015). Student research and report writing : From topic selection to the complete paper. Retrieved from http://ebookcentral.proquest.com
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Steps of Quantitative and Qualitative Research Designs 129
1 Do you disagree that letter grades for college courses shouldn’t be
abolished?
___ strongly agree ___ agree ___ disagree ___ strongly disagree ___ don’t
know
Problem:
Rewrite:
2 Do you think that cigarette smoking is harmful to health and favor laws
that ban smoking in all public places?
___ strongly agree ___ agree ___ disagree ___ strongly disagree ___ don’t
know
Problem:
Rewrite:
3 How often do you attend religious services?
___ more than once a week ___ once a week ___ a couple of times a year
___ on holidays
Problem:
Rewrite:
4 What subject fi eld is your major?
___sociology ___psychology ___social science ___science ___other
Problem:
Rewrite:
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130 Student Research and Report Writing
Exercise 6.4 Questionnaire Design; Final Review of the
Questionnaire
Once you fi nish writing survey questions/measures for all of your variables it is
time to put them together and review them for fl ow. This is a checklist for review-
ing your survey instrument.
1 Does my questionnaire have an introduction that includes informed consent
statements?
2 Does my questionnaire include ALL of the variables for my research?
3 Are questions clearly written and free of grammatical errors?
4 Is there biased language that may infl uence the respondent’s answer?
5 Is the spacing of the questions adequate? Does the questionnaire look visually
professional (in case you are using print surveys)?
6 Does my questionnaire have proper transitional directions for the respondent?
(If your survey questionnaire has several parts, you should have a transitional
sentence to take your respondents from one part to another. For example, after
asking about school life, tell your respondents “Next, I would like to ask you
questions about your family life” before inquiring about family life. In this way,
you help your respondents move from one section of questions to another and
enable them to answer your questions more easily and accurately.)
7 How long will it take an average respondent to complete this questionnaire? Is
the time you ask of respondents adequate and reasonable?
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Steps of Quantitative and Qualitative Research Designs 131
Exercise 6.5 Evaluating Preparedness for Interviews
Questions for semi‐structured and structured interviews and focus group inter-
view questions can be evaluated using the following guidelines.
1 Have I prepared a proper introduction to establish a comfortable and safe
environment for the interviewees? (e.g., Explain who I am, the purpose of the
interviews or focus groups, and what I ask of the participants.)
2 Have I prepared an informed consent form for the participants?
3 Am I familiar with the culture of the participants and prepared to communi-
cate comfortably with them? Am I sure I have no biased or judgmental opin-
ions about the participants or the communities I am about to study?
4 Does my interview schedule cover all of the themes and issues relevant to the
research questions of my study?
5 Are my interview questions open‐ended? Are they phrased in such a way to
encourage detailed stories and answers?
6 Does any of the questions lead to simple yes or no answers? (If so, revise
them.)
7 Do I have probing questions?
8 Are the questions written in non‐biased language?
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132 Student Research and Report Writing
9 Does the interview schedule have a smooth fl ow? Are the transitions from one
question to the next natural?
10 Am I prepared to ask the questions in several diff erent ways in case the inter-
viewee does not understand them?
11 Do I have a functioning voice‐recording device and have an access to a tran-
scribing machine?
12 Have I obtained ethical clearance on my project?
Your Project Outcome after Chapter 6
At this point:
• You have clearly specifi ed the defi nitions of the concepts used in your
research questions and hypotheses.
• You have determined which data collection methods you will use.
• You have identifi ed the study population and determined the sampling
strategy to select participants from this population.
• You have constructed your survey questionnaire, or pre‐ and post‐test
measures.
• You have established contacts with the site or the community in which
you wish to conduct qualitative research.
Wang, G. T., & Park, K. (2015). Student research and report writing : From topic selection to the complete paper. Retrieved from http://ebookcentral.proquest.com
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