Inferential AnalysisChapter 20NUR 6812Nursing Research

Inferential Analysis
Chapter 20
NUR 6812Nursing Research
Florida National University
Introduction - Inferential Analysis
We will discuss analysis of variance and regression, which are
technically part of the same family of statistics known as the
general linear method but are used to achieve different
analytical goals
ANALYSIS OF VARIANCE
Analysis of variance (ANOVA) is used so often that Iversen and
Norpoth (1987) said they once had a student who thought this
was the name of an Italian statistician.
You can think of analysis of variance as a whole family of
procedures beginning with the simple and frequently used t-test
and becoming quite complicated with the use of multiple

dependent variables (MANOVA, to be explained later in this
chapter) and covariates.
Although the simpler varieties of these statistics can actually be
calculated by hand, it is assumed that you will use a statistical
software package for your calculations.
If you want to see how these calculations are done, you could
try to compute a correlation, chi-square, t-test, or ANOVA
yourself (see Yuker, 1958; Field, 2009), but in general it is too
time consuming and too subject to human error to do these by
hand.
IMPORTANT TERMINOLOGY
Several terms are used in these analyses that you need to be
familiar with to understand the analyses themselves and the
results. Many will already be familiar to you.
Statistical significance: This indicates the probability that the
differences found are a result of error, not the treatment. Stated
in terms of the P value, the convention is to accept either a 1%
(P ≤ 0.01), or 1 out of 100, or 5% (P ≤ 0.05), or 5 out of 100,
possibility that any differences seen could have been due to
error (Cortina & Dunlap, 2007).
Research hypothesis: A research hypothesis is a declarative
statement of the expected relationship between the dependent
and independent variable(s).
Null hypothesis: The null hypothesis, based on the research
hypothesis, states that the predicted relationships will not be
found or that those found could have occurred by chance,

meaning the difference will not be statistically significant.
Effect size: This is defined by Cortina and Dunlap as “the
amount of variance in one variable accounted for by another in
the sample at hand” (2007, p. 231). Effect size estimates are
helpful adjuncts to significance testing. An important
limitation, however, is that they are heavily influenced by the
type of treatment or manipulation that occurred and the
measures that are used.
Confidence intervals: Although sometimes suggested as an
adjunct or replacement for the significance level, confidence
intervals are determined in part by the alpha (significance level)
(Cortina & Dunlap, 2007). Likened to a margin of error, the
confidence intervals indicate the range within which the true
difference between means may lie. A narrow confidence interval
implies high precision; we can specify believable values within
a narrow range. A wide interval implies poor precision; we can
only specify believable values within a broad and generally
uninformative range.
Degrees of freedom: In their most simple form, degrees of
freedom are 1 less than the total number of observations. This
sometimes-confusing term refers to the smallest number of
values (terms) that one must know to determine the remaining
values (terms). For example, if you know the weights of 12 out
of a sample of 13 people and also the sum (grand total) of the
weights of these 13 people, you can easily calculate the weight
of the 13th person. In this case, the degrees of freedom would
be 12, or 13 minus 1 degree of freedom. If you had a second
sample of 13 people and again needed to know the weights of 12
to calculate the 13th, the degrees of freedom for these two
subsamples together would be 12 + 12 = 24. Not all calculations
of degrees of freedom are this simple, but they are based on this
principle (Iversen & Norpoth, 1987; Keppel, 2004).
Variance: This is a measure of the dispersion of scores around
the mean, or how much they are spread out around the mean.
Statistically, it equals the square of the standard deviation
(Iversen & Norpoth, 1987; Munro, 2005).

Mean: The mean is the arithmetic average of a set of numbers,
usually the scores or other results for a sample or subsample.
This is simple to calculate by hand unless you have a very large
sample. Variable: A variable is a characteristic or phenomenon
that can vary from one subject to another or from one time to
another (O’Rourke, Hatcher, & Stepanski, 2005).
Independent variable: In experimental research, the independent
variable is the treatment or manipulation that occurs. In
nonexperimental research, it is the theoretical causative factor
that affects the dependent or outcome variable. In other words,
it is the explanatory variable, also called the predictor variable.
Dependent variable: In experimental research, the dependent
variable is the measured outcome of the treatment (in the
broadest sense of the term treatment). In nonexperimental
research, the dependent variable is the theoretical result of the
effects of the independent variable(s). It is also called the
criterion variable.
T-Tests
The cardinal feature of t-tests and ANOVAs also provides an
important clue to their usage: these statistical procedures
analyze the means of at least one continuous (interval or ratio)
response variable in terms of the levels of a categorical
variable, which has the role of predictor or independent variable
(Der & Everitt, 2006).
The simplest of these statistics are t-tests. They may be used
under the following conditions:
There is just one predictor or independent variable that has just
two values, such as male/female, treated/not treated, or hospital

#1 patients/hospital #2 patients.
There is a single criterion or dependent variable measured at the
interval or ratio level. You can see that the applicability of the
t-test is limited by these criteria. In most cases that do not fit
these criteria, ANOVA becomes the procedure of choice. There
are two common types of t-tests (O’Rourke et al., 2005):
Independent samples: This type of t-test is appropriate when
there are two subsamples being compared on an outcome
measure. For example, you might randomly assign severe
asthma patients to an environmental control education program
or a general asthma education program and compare the number
of times they used their rescue inhalers in the 3 months
following intervention. (Note that this is a posttest-only design;
there is no pretest.)
Paired sample: This type of t-test is appropriate when the same
subjects constitute each sample being compared under two
different sets of conditions. Because they are the same people,
the results are obviously not independent of one another and are
said to be paired or correlated. For example, you could compare
severe asthma patients’ use of rescue inhalers before and after
they attend an educational program on environmental control.
(Note that this is a one-group pretest-posttest design.)
T-tests may be used in nonexperimental situations as well. Most
common is a comparison of naturally occurring groups or events
such as the difference between male and female students’
mathematical abilities (an example of independent samples) or a
comparison of marital discord scores before and after the birth
of the first child (an example of paired samples). An example of
each of these t-tests will help to clarify terms and demonstrate
their use.
Independent Samples
Independent samples are samples that are selected randomly so
that its observations do not depend on the values other

observations.
Many statistical analyses are based on the assumption that
samples are independent.
Others are designed to assess samples that are not independent.
Paired Samples
A paired samples t-test is used to compare the means of two
samples when each observation in one sample can be paired
with an observation in the other sample.
A paired samples t-test is commonly used in two scenarios:
1. A measurement is taken on a subject before and after some
treatment – e.g., the max vertical jump of college basketball
players is measured before and after participating in a training
program.
2. A measurement is taken under two different conditions – e.g.,
the response time of a patient is measured on two different
drugs.
In both cases we are interested in comparing the mean
measurement between two groups in which each observation in
one sample can be paired with an observation in the other
sample.

Paired Samples t-test: Assumptions
For the results of a paired samples t-test to be valid, the
following assumptions should be met:
The participants should be selected randomly from the
population.
The differences between the pairs should be approximately
normally distributed.
There should be no extreme outliers in the differences.
ANOVA Analysis of Variance
ANOVA Analysis of variance extends the t-test to three or more
groups. It is especially useful in examining the impact of
different treatments (Muller & Fetterman, 2002).
If you had three subsamples to compare on one outcome
measure, you could do this with a set of three t-tests, but this
approach is inefficient and increases the risk of type I error.

Instead, analysis of variance performs these comparisons
simultaneously and produces a significant result if any of the
sample means differ significantly from any other sample mean
(Evans, 1996, p. 339).
ANOVA compares the variation or difference between the
means of the subsamples or groups with how much variation
there is within each group or sub-sample (Iversen & Norpoth,
1987, p. 25).
F Ratio
Analysis of variance computations produce an F ratio. There is
usually variation within the groups as well as between the
groups. An F ratio is the ratio of between-treatment group
variations to within-treatment group variation. F ratios close to
1 indicate the differences are random or chance differences. F
ratios much larger than 1 indicate that the difference is greater
than would be expected by chance.
One-Way ANOVA
One-way ANOVA is the basic analysis of variance. It involves
(1) a single predictor or independent variable that is categorical
in nature but may have two or more values, and (2) a single
criterion or dependent variable at the interval or ratio level of
measurement (O’Rourke et al., 2005, p. 210). One-way ANOVA
involves only one predictor variable. As with t-tests, there are
two basic types, a between-subjects model, which is similar to
the independent sample t-test, and a repeated-measures model,
which is similar to the paired t-test

Repeated Measures Designs These designs are also called
within-subjects designs because more than one measurement is
obtained on each participant. The simplest of these designs is
the testing of the same participants under two or more different
treatment conditions.
Advantages of this design, which uses participants as their own
controls, are that fewer participants are needed and the
treatment groups do not differ (Munro, 2005). These
advantages, however, are often outweighed by the following
disadvantages:
High attrition rate: A large number of participants are lost from
the study between the two treatment conditions.
Order effect: Participants may not be as enthusiastic about
trying the second or third treatment option, reducing adherence.
Carryover effect: Participants may continue to experience or
benefit from the effects of the first treatment (O’Rourke et al.,
2005).

Mixed Designs
A second repeated measures design uses different participants
in each treatment group.
This eliminates order and carryover effects, but it does mean
that the participants in each treatment group will not be
identical. Even with random assignment to treatment group,
there will probably be some variation between groups at
baseline.
This second repeated measures design is called a mixed design
because it will generate both between-group (the different
treatment groups) and within-group (change or lack of change
from one time to another) measures.
The analysis of the results will provide three types of
information:
Change over time
Differences between the groups
The interaction of time and group effects (Munro, 2005)

ANCOVA Analysis of Covariance
ANCOVA Analysis of covariance is a procedure in which the
effects of factors called covariates are extracted or controlled
before the analysis of variance is done (Der & Everitt, 2006).
The covariates are often confounding variables or extraneous
variables that contribute to the variation and reduce the
magnitude of the differences between the groups being
compared.
Controlling for these extraneous or confounding variables can
reduce the error variance and increase the power of the analysis
(Munro, 2005).
There are two main instances when ANCOVA is used:
1. When a variable is known to have an effect on the dependent
(outcome) variable in an analysis of variance
2. When the groups being compared are not equivalent on one
or more variables, either because they were not randomized or
in spite of randomization (Munro, 2005, p. 200)
Two-Way ANOVA

The ANOVA-based analyses discussed so far have employed a
single independent variable at the nominal or categorical level
of measurement.
(The independent variable is the treatment variable in
experimental research or the explanatory variable in
nonexperimental research.)
Two-way analysis of variance allows you to examine the effects
of two between-subjects independent variables at once,
including the interaction between the two independent variables
(Munro, 2005; O’Rourke et al., 2005).
MANOVA
MANOVA One additional procedure from the analysis of
variance family, often a very useful one, is the multivariate
analysis of variance (MANOVA).
You have encountered mention of avoiding type I error
(rejecting the null hypothesis when it is true) several times
already in this chapter.
When you have a number of criteria or outcome variables that

are conceptually related, instead of analyzing each one
separately using ANOVA, you can begin the analysis with a
MANOVA.
REGRESSION ANALYSIS
In this second half of the chapter, we will focus on prediction of
the dependent variable based on knowledge of the independent
variable rather than on comparison of means.
The discussion will be limited to the most basic and commonly
used linear regression analyses. Regression analyses can
become very complex in some of their iterations. You will find
these discussed in advanced statistics textbooks.
The primary assumption behind linear regression analysis is
clearly described by Evans (1996):
Its most essential assumption is that variables x and y have a
straight-line relationship with each other…. If that assumption
is true for a set of pairs of scores, then y values can be
predicted from x values. The stronger the correlation between x
and y, the more accurate the predictions (p. 160).
The x variable, by the way, is the predictor (independent)
variable, and the y variable is the criterion (dependent)
variable. We can do much more than this with regression, but

this is the fundamental basis of regression: to predict values of
y from values of x.
Simple Linear Regression
There is an interesting and deceptively simple set of cognitive
function tests called the category fluency tests.
To administer the test, the examiner asks the person being
tested to name as many animals or as many fruits, vegetables,
words beginning with F, modes of transportation, items of
clothing, or other categories as possible in 1 minute.
The answers are recorded, and the score is simply the number of
relevant, nonredundant items or words generated in 1 minute.
The simplicity of the test makes it easy to understand.
The number of factors that might influence the total score
makes it an interesting example to illustrate linear regression
analysis.
Multiple Regression with Two or More Independent Variables
Multiple regression is used to examine the “collective and
separate effects of two or more independent variables on a

dependent variable” (Pedhazur, 1982, p. 6).
The discussion will begin with the use of continuous (interval
or ratio level) independent variables and then address the use of
nominal-level (categorical) independent variables through what
is called dummy coding or effect coding.
Multicollinearity
The choice of independent variables to include in a regression
equation is often a challenge for the researcher.
Theory underlying the study, the results of prior studies, and the
study hypothesis should guide the selection.
It is tempting to include as many variables as possible to boost
the R2 and improve the predictive power of the equation, but
this approach increases the risk of multicollinearity among the
independent variables.
Collinearity may be defined as redundancy among the variables.
In other words, some of the independent variables added to a
regression equation may contribute little to the information that
has already been contributed by other variables.

Dummy Coding
After encountering all those difficult technical terms, this next
one, dummy coding, might provide some comic relief.
Despite its odd name, however, dummy coding extends the
reach of multiple regression in some very useful ways.
Up to this point, all of the variables entered into the regression
analyses have been continuous variables, measured at the
interval or higher level. (In some cases, ordinal variables can
also be used.)
Dummy coding allows us to include nominal or categorical level
variables (called qualitative in some texts) as well.
Selection

You could see in the previous section on dummy coding that
entering additional independent variables can have an effect on
both the weight of other variables and the overall results of the
regression.
As mentioned earlier, selecting the variables to enter into the
regression equation and deciding which ones should be retained
is often a challenge.
Theory and prior research results should be your primary
guides, but they do not always provide enough guidance.
Another approach is to use the results of exploratory analysis to
make these decisions.
There are a number of different selections you can make to
conduct this exploratory regression analysis:
Maximum R2: This analysis begins by selecting one or two
variables that produce the highest R2, interchanges them until
those with maximum improvement in R2 are identified, and then
brings in additional variables and interchanges them until the
optimum combination is found.
Forward selection: This analysis begins with the simple (one-
variable) model that produces the largest R2 and adds variables
until no further increase is found. Selected variables are not
deleted later in this approach.
Backward elimination: This analysis begins with all of the
variables in the regression and then deletes those with the least
significance, one at a time, until all remaining variables are at
the prespecified level of significance.
Stepwise: This analysis begins with the one-variable model that
produces the largest R2, and then adds and deletes variables
until no further improvement can be made.
Hierarchical Linear Regression

Hierarchical Linear Regression
Instead of putting all the variables into the analysis at once, as
is done in multiple regressions, or entering them in an order
determined by preset limits such as significance level,
hierarchical linear regression employs a series of steps or
blocks of variables determined in advance on a theoretical
basis.
This is an advanced technique that will not be described in
detail but summarized so the reader will have a general idea of
when it is an appropriate choice and how it works.
Sample Size
The temptation to include (some would say “throw in”) as many
variables as possible in the regression equation and the
problems associated with doing this have already been
mentioned but are emphasized once more when considering the
sample size needed to conduct these analyses.
A common rule of thumb is to have at least 10 subjects per
variable in the analysis (Munro, 2005). Any fewer than that will
result in unstable outcomes and appreciable shrinkage of the

adjusted R2.
You can also conduct a power analysis to determine the sample
size needed. You can find the formulas to do this in Cohen
(1988) or generate a power analysis from your statistical
analysis program.
Logistic Regression
Up to this point, we have addressed regression of independent
variables on a continuous dependent variable.
Logistic regression addresses the use of a categorical dependent
variable in the equation.
If this variable is dichotomous (having only two different
values), then logistic regression is done.
If the dependent variable has three or more values, then a
polytomous regression is done.
These analyses may also be done with dependent variables that
can be ordered

CONCLUSION
One of the best ways to gain an appreciation of the analytic
procedures described in this chapter is to apply them to a
dataset—your own, if possible, or one of the demonstration
datasets that accompany most software packages and statistical
textbooks.
Each of these procedures has its uses but also its drawbacks.
It is important to understand both when you apply them in your
research.
Obtaining guidance from an experienced researcher and/or
statistician will help you not only select the most powerful
procedures for your dataset, but also avoid inappropriate
applications of them.
Reference
Tappen, R. M. (2015). Advanced Nursing Research. Chapter 20
[VitalSource Bookshelf]. Retrieved from
https://bookshelf.vitalsource.com/#/books/9781284132496/

Analysis of Qualitative Data
Chapter 21
NUR 6812 Nursing Research
Florida National University
Introduction
The most structured approach to the analysis of qualitative data
uses coding and quantifying of the qualitative data.
Content analysis is a specific case of the quantification of
qualitative data. At the other end of the continuum are three of
the great traditions in qualitative research: ethnography,
grounded theory, and phenomenological analysis.
In between lie a variety of coding and thematizing analyses that
operate within a semistructured and unstructured framework.
Each of these reflects a very different tradition, which needs to
be kept in mind as you match your data to the appropriate
analytic framework.
Data collection and data analysis may occur virtually
simultaneously in the most unstructured of these approaches,
whereas the more structured analyses are done after the data
have been collected and processed.
PROCESSING THE DATA

Faced with a mountain of observational notes and transcribed
conversations, many qualitative researchers have thought to
themselves,
“What do I do now?” Handling that mountain of qualitative data
requires some organization.
There are several activities you may need to complete during
the data collection and analysis stages to manage the data and
facilitate the final analysis.
PROCESSING THE DATA
Organize Your Material Keep notes organized with sources and
time frames clearly identified
Prepare accurate transcriptions of notes and recordings. This
step is not an absolute necessity if you (1) plan to hand code (as
opposed to using a software program), (2) will do all the
analysis yourself, (3) write clearly, and (4) do not have a

mountain of data.
Upload Data for Analysis
Upload the texts.
Maintain lists of the codes you have created.
Mark text by code or theme.
Retrieve text that you have coded.
Support creation of a hierarchy of codes and themes.
Allow you to write memos and attach them to text.
WHY QUANTIFY? Even in qualitative research, there are
occasions where counting is useful, including the following:
To describe the sample
To report the frequency of a response
To compare frequencies across subgroups

To combine quantified qualitative data with other quantitative
data:
STRUCTURED AND SEMISTRUCTURED ANALYSIS Coding
Coding is “a deliberate and thoughtful process of categorizing
the content of the text” (Gibbs, 2007, p. 39). Two purposes for
coding will be discussed.
Coding of responses to structured and semistructured questions
for the purpose of quantifying them is our immediate interest.
Later in the chapter, the coding of text for qualitative analysis
will be illustrated. Even more specific, Miles, Huberman, and
Saldaña describe codes as tags or “labels that assign symbolic
meaning to the descriptive or inferential information compiled
during a study” (2014, p. 71).
They identify several types of codes that can be affixed to
responses or portions of text:
Descriptive codes: These are the most concrete level of labeling
data that simply divide data into various categories or groups of

phenomena. In most qualitative studies, you will want to use the
respondent’s own words as much as possible to preserve their
language.
Interpretive codes: These codes are more abstract and are based
on your understanding of the meaning underlying what has been
said or done.
Pattern codes: Even more abstract and complex, these codes
suggest connections between various patterns or meanings and
are indicative of possible themes within the data (Miles &
Huberman, 1994).
A simple example will be used to illustrate the use of coding to
quantify qualitative data at relatively concrete levels of
analysis—the descriptive and interpretive.
CONTENT ANALYSIS
Content analysis and its kin, narrative, conversation, and
discourse analysis, are a special case in qualitative analysis.
Content analysis is “a family of analytic approaches ranging
from impressionistic, intuitive, interpretive analysis to
systematic, strict textual analyses” (Hsieh & Shannon, 2007, p.
61).
In other words, content analysis may range from highly
structured, quantitative analysis to unstructured qualitative
analysis.
Words in naturally occurring verbal material (text), whether
recorded conversation, diaries, reports, electronic text, or
books, constitute the data used in content analysis (McTavish &
Pirro, 2007, p. 217).

ANALYZING THE TEXT
Selecting an approach to the analysis of text begins with a well-
constructed research question. As Krippendorff (2004) reminds
us, texts convey many different meanings. Several questions
need to be answered in constructing the research question:
• What am I looking for in this text?
• Is the focus on content, interpersonal interaction, or both?
• Will I be working at the micro level or macro level of
analysis? How micro or macro?
• What type of content analysis best answers my question? ◦
Quantitative or qualitative?
◦ Analysis of the story or experience or the interactional
processes that occur?
◦ Strong emphasis on the influence of context or minimal
attention to specific context?
ANALYZING THE TEXT
Data Transcription
Data transcription should reflect the analytical purpose. For
some purposes, especially conversation and linguistic analyses,
you need to have every inflection, pause, vocalization, and
contraction noted precisely.
Data Exploration
After transcription is completed and checked for accuracy, your
next step is to read and reread the entire dataset, making notes

on general impressions, possible coding schemes, and the
different perspectives from which you could analyze the data. If
you plan to do a conversational analysis, ten Have (1999)
suggests looking at turn-taking including pauses and overlaps;
sequencing including the beginning and end of a particular
sequence or “chunk” of the conversation that follows a specific
thread; what each participant is doing on each turn and the form
chosen
Completing the Analysis
Once the coding scheme has been created, it is time to apply it
to the entire dataset. If the dataset is large, use of a qualitative
data analysis program is very helpful. Following is a list of
some of the activities this type of program can support
(Krippendorff, 2004, p. 262):
Dividing the text into analytical units: These units could be
syllables, words, phrases, sentences, or even paragraphs.
Searching the text: Find, list, sort, count, retrieve, and cross -
tabulate the identified analytical units (Krippendorff, 2004, p.
262).
Computational content analysis: The results of the coding can
be analyzed quantitatively in some cases.
Interactive hermeneutic approaches: This is an interpretive
approach to the analysis. Second- and third-level coding is
supported by most qualitative analysis programs.
UNSTRUCTURED ANALYSIS
The goal of most qualitative analysis is to move beyond the
most concrete, descriptive level to higher levels of abstraction
and interpretation, identifying the themes and sometimes
constructing new concepts or theoretical propositions from the
results.

ETHNOGRAPHIC ANALYSIS
The ethnographic approach to data collection and analysis is
designed to achieve understanding of other cultures.
Originally employed by anthropologists who often devoted
years to the study of remote places and people, it has also been
used to study subcultures closer to home: street people, gay and
lesbian groups, hospital environments, health beliefs of
minority and disadvantaged populations, and so forth.
Coding
Analysis begins with the first interview or observation
undertaken in an ethnographic study (Gobo, 2008).
In fact, it should begin even earlier, as you are negotiating entry
into the field. Your notes taken at that time and thoughts about

what you are seeing and experiencing are the beginning of your
data collection and analysis.
The data that you have collected should provide the “thick
description” that Geertz (1973) recommended (deriving the
concept from Ryle, 1971), and you need to try to remain as
faithful to informants’ perspectives as possible throughout data
collection and data analysis (Wolf, 2007, p. 32).
Interpretation
As you code, create categories and search for themes. Bernard
(1988) reminds us to be constantly checking and rechecking the
ideas that are forming. In particular, look for the following:
Inconsistencies in statements of different informants may reflect
real differences, differences in perspective, or misunderstanding
on your part. The inconsistencies need to be checked out and
corroborating evidence sought.
Similarly, negative evidence should not be ignored. Instead, it
should be evaluated and explained.
Alternative explanations should be considered. For example, it
is generally assumed in Western societies that more women are
working now because of the emphasis on equality and women’s
rights. An alternative explanation would be that they were
forced to work because the purchasing power of their spouses’
income was declining, or their families’ expectations were
rising faster than one income could accommodate (Bernard,
1988).
Also, consider the extreme cases and why they are extreme: do
they represent outliers, or are they markers for the far ends of a

continuum?
Additional Strategies
Additional Strategies There are several more complex analytic
strategies that go beyond simple coding schemes:
Domain analysis, structural and contrast questions, taxonomic
analysis, and componential analysis (Bernard, 1988; Spradley,
1979).
We will consider each of these briefly to give you an idea of
what can be done in an in-depth ethnographic analysis.
All of these analyses are based on the assumption that there is a
system of symbols within a culture or subculture and that the
relationships between symbols can be discovered (Spradley,
1979).
Domain analysis: We have already talked about creating
categories. Domains can be thought of as sets of categories
within categories.
Structural and contrast questions: These questions lead to
further exploration of a particular domain. The structural
questions explore what is included within a category or domain.
Taxonomies: When you begin putting together the information
from all of these questions, the relationships can become very
complex. Bernard (1988, p. 337) points out that we use “folk”
taxonomies all the time.
Componential analysis: Componential analysis addresses the
various attributes of a cultural symbol (Spradley, 1979, p. 174).
Writing the Narrative
Writing the final narrative is an important part of completing

the analysis.
New insights may occur to you even as you are writing what
you think are the final results.
This is not unusual and should not be considered a fault in your
analysis.
GROUNDED THEORY
Grounded theory is distinguished from other types of qualitative
research by its method and intent, which is to develop theory
grounded in data (Corbin, 2009, p. 52), particularly middle-
range theory (Charmaz, 2001).
Constant Comparison Method
In grounded theory, data collection and analysis are not done
separately or sequentially, one after the other.
Instead, they are part of a cyclical process that moves from data
collection to analysis and back to data collection (Boeije, 2007).
What is learned in the first interview, for example, guides the
types of questions asked at the next interview.
GROUNDED THEORY
Coding
Coding emerges from the data. Charmaz (2006) recommends
staying close to the data when doing the initial coding and using
action language whenever possible, not topics as was done in
the generic coding of the interview illustrated earlier. Action

coding uses changing instead of change, responding instead of
response, experiencing instead of experience, and fulfilling
instead of fulfillment. This is done to stay as close to the data
as possible and to avoid applying existing ideas and concepts
instead of allowing them to emerge from the data. Stern (2009)
also warns against using “pet codes”—favorites that are not
necessarily the best fit for your data.
Axial coding brings the data together again, although in a
different form. Large amounts of data are synthesized,
connections and links are identified, and dimensions are
described. One framework for doing this considers the
following (Charmaz, 2006, p. 61; Corbin & Strauss, 2007, p.
101):
• Conditions that lead to the phenomenon
• The context in which it occurs
• Responses of the participants to this phenomenon
• Outcomes of the responses
GROUNDED THEORY
Theoretical Sampling
Although the researcher begins a grounded theory study with a
general idea of what the sample should be like, the specifics of
exactly who should be interviewed and how many should be
interviewed evolve as the study progresses
Saturation
Unlike most quantitative research studies in which the desired
number of participants is specified at the outset, the sample size
in unstructured qualitative research is somewhat indeterminate
at the outset. When employing the grounded theory approach,

the goal related to sample size is to include as many participants
as necessary to achieve saturation. How do you know this has
been achieved? Saturation is achieved when new cases do not
bring any new information or insights to light.
Writing Theory
When you have completed coding at all three levels, have an
ample set of memos, have achieved that higher level of
abstraction we call theorizing, and you have then completed a
process of induction proceeding from many disparate bits and
chunks of data to a reconstituted, explanatory whole, you are
ready to begin writing theory (Glaser & Strauss, 1967).
To simplify this somewhat, the codes and categories provide the
structure, and the memos provide the explanation and
discussion. Too often, researchers provide the results without
walking the reader through the thought processes that led to the
results.
This lack of transparency reduces the reader’s ability to
evaluate the rigor of the analyses and the trustworthiness of the
findings. Corbin and Strauss (2007) offer a set of criteria for
evaluating the outcome of a grounded theory study. They
provide a helpful guide for both the researcher and the reader of
the final product, whether a report, journal article, or book (pp.
106–107):
• Were concepts generated?
• Are the concepts systematically related?
• Are the categories dense and well developed?
• Is variation (i.e., the degree to which a phenomenon varies
between groups) addressed?
• Are macrosocial conditions and context linked to the
phenomenon?
• Do the findings have significance in terms of explaining a
phenomenon or suggesting direction for further research? These
questions are designed to address how well the reported results

are grounded in the data.
PHENOMENOLOGICAL ANALYSIS
This approach to qualitative research is closely tied to the
underlying philosophies that have informed their development.
Those who use this method without the philosophical
underpinnings are frequently criticized for doing so.
Many nurse researchers find phenomenological analysis
intuitively appealing because it focuses on the individual’s
experience and the context of that experience.
This is a central concern for nursing, thus its frequent use in
qualitative nursing research.
Two Schools of Thought
Two Schools of Thought It may come as no surprise to you to
find that there is more than one school of thought within
phenomenology.
In fact, as many as 18 different forms of phenomenology have
been identified.
The first is the eidetic or descriptive tradition derived from
Husserl’s philosophy.
No specific research question or hypothesis is formulated when
launching this type of inquiry, but identification of the
phenomenon of interest is appropriate.
Bracketing is an essential part of this approach.
To do this, the researcher refrains from searching the literature
before embarking on the study.

The interpretive tradition, derived from the works of Heidegger,
goes beyond description to search for “meanings embedded in
common life practices” (Lopez & Willis, 2004, p. 728).
This is the interpretive aspect of the approach, moving beyond
the words of the participant to seek the meaning in them.
Context is part of this analysis because the way we experience a
phenomenon, whether it is chronic pain, being overweight, or
adopting a child, is influenced by the people and circumstances
surrounding us, our life world (Lopez & Willis, 2004, p. 729).
Instead of bracketing or setting aside experience and personal
opinion, the interpretive phenomenological researcher can use
them as guides to shaping the inquiry.
Further, a theoretical perspective or framework may be used so
long as it is made clear that it is the lens through which this
experience was viewed.
In fact, Dahlberg and colleagues (2001) suggest you keep
several possible theories in mind and “let them compete” (p.
208) during the analysis phase.
The interpretation, then, becomes a “blend” of the meanings of
the researcher and the participants, which is called
intersubjectivity (Lopez & Willis, 2004, p. 730).
The interpretive tradition, derived from the works of Heidegger,
goes beyond description to search for “meanings embedded in

common life practices” (Lopez & Willis, 2004, p. 728).
This is the interpretive aspect of the approach, moving beyond
the words of the participant to seek the meaning in them.
Context is part of this analysis because the way we experience a
phenomenon, whether it is chronic pain, being overweight, or
adopting a child, is influenced by the people and circumstances
surrounding us, our life world (Lopez & Willis, 2004, p. 729).
Instead of bracketing or setting aside experience and personal
opinion, the interpretive phenomenological researcher can use
them as guides to shaping the inquiry.
Further, a theoretical perspective or framework may be used so
long as it is made clear that it is the lens through which this
experience was viewed.
In fact, Dahlberg and colleagues (2001) suggest you keep
several possible theories in mind and “let them compete” (p.
208) during the analysis phase.
The interpretation, then, becomes a “blend” of the meanings of
the researcher and the participants, which is called
intersubjectivity (Lopez & Willis, 2004, p. 730).
Planning and Conducting the Inquiry
Munhall (2010) and van Manen (1990) have described the
conduct of a phenomenological inquiry in very clear terms,
which are sometimes difficult to find in writing about
phenomenology. The following sections outline the process of
phenomenological inquiry.
Immersion
First, you must become well acquainted with the underlying

philosophy and be certain there is a good fit between that
philosophy and your approach. There are multiple ways to look
at the world and to interpret the meaning of people’s
experiences in the world. You may need to begin with
secondary sources to help you understand the many forms of
phenomenology. Consider beginning with the work of Munhall
(2010) and van Manen (1990), whose writing is accessible.
Aim of the Inquiry
An aim is a more general statement than a research question or
hypothesis. It defines the focus of your study as well as the
context. For example, you might be interested in the experience
of adopting a child. This is an appropriate topic but so broad
that it needs some delimiters. So, with further thought about the
subject and reflection on why you chose it for your inquiry, you
might add these delimiters: international adoption of a child
with chronic health problems by a single parent.
Inquiry and Processing
As is done in grounded theory, these are done concurrently.
Interviews are fundamental to the inquiry, but observations,
reading of texts, and other sources of information may be used.
The interviews or dialogues are transcribed, and the text is
analyzed; however, this is not an analysis of the language used,
as it might be in a discourse analysis, but an analysis of the
phenomenon under study
Analysis
Remember that the purpose of the interpretive type of

phenomenological study is not to just relate the facts of an
experience (describe the phenomenon) but to discern the
meaning of the experience as well.
This is eventually communicated to others through writing, one
reason why writing pieces of the narrative begins so early in
this process.
The analysis phase is one of reading, reflecting, discussing, and
writing. Van Manen (1990) speaks of reflecting
phenomenologically, attempting to grasp the essence of the
experience (p. 78).
Discussion may take place with colleagues, members of the
research team, or participants. Reading may be limited to
related theoretical and research publications or may be extended
to experiential literature.
The overarching theme or meaning of the nurses’ role change
was expressed as their having experienced moments of
excellence that “gave nurses a renewed passion for professional
practice as they realize the impact they had on their patients’
lives” (Turkel et al., 1999, p. 11).
Hermeneutic Writing

Writing begins early in the process of a phenomenological
inquiry. The goal of the final product is to permit us to see the
“deeper significance or meaning structures of the lived
experience it describes” (van Manen, 1990, p. 822).
Misunderstandings and Misconceptions
About Phenomenological Inquiry Norlyk and Harder (2010)
reviewed 38 articles identified by the authors as having been
done using the phenomenological method of research. They
found a number of misunderstandings and misconceptions about
the phenomenological approach that may provide some useful
reminders for other researchers.
They remind the researchers to:
• Make clear how the study is phenomenological in its
approach.
• Distinguish between descriptive and interpretive inquiry.
• Identify the philosophical assumptions on which the study is
based.
• Remember that quotes from participants support but do not
replace narrative.

• Make sure the aims are appropriate to phenomenological
inquiry. Purposes that are generally not appropriate include
finding or testing solutions to identified problems.
• Do not use terminology specific to other traditions such as
theoretical saturation from grounded theory or selection bias
from quantitative sampling. In general, researchers are
cautioned to use the appropriate methods and terminology
specific to phenomenology if they choose to conduct a
phenomenological inquiry.
Summary
Although there are commonalities among the many types of
qualitative analysis described in this chapter, each approach has
its distinctive features, methods, goals, and terminology.
Many are based on long-established traditions that should be
respected when using them.
Others are more contemporary, borrowing the most useful
strategies from the more traditional approaches.
To some beginning researchers, qualitative analysis appears
easier to accomplish than does quantitative analysis.
This is deceptive. An elegant, insightful qualitative analysis is
like a work of art: inspiring the beholder (the reader) but
representing a mighty effort on the part of the artist (the
researcher).
References

Charmaz, K. (2006). Constructing grounded theory: A practical
guide through qualitative analysis. Los Angeles, CA: Sage.
Charmaz, K. (2001). Qualitative interviewing and grounded
theory analysis. In J. F. Gubrium & J. A. Holstein (Eds.), The
Sage handbook of interview research (pp. 675–694). Thousand
Oaks, CA: Sage.
Morse, J. M. (1992). Qualitative health research. Newbury Park,
NJ: Sage.
Morse, J. M., Stern, P. N., Corbin, J., Bowers, B., Charmaz, K.,
& Clarke, A. E. (2009). Developing grounded theory: The
second generation. Walnut Creek, CA: Left Coast Press. ✧ This
book contains some excellent examples of qualitative studies
done within each of the great traditions and more. The classic
“On Being Sane in an Insane Place” by Rosenhan is just one
example.
Munhall, P. L. (2010). A phenomenological method. In P. L.
Munhall (Ed.), Nursing research: A qualitative perspective (pp.
113–175). Sudbury, MA: Jones and Bartlett. ✧ The review of
the process of conducting a phenomenological study from
beginning to end is very helpful and easy to follow.
Norlyk, A., & Harder, I. (2010). What makes a
phenomenological study phenomenological? An analysis of
peer-reviewed empirical nursing studies. Qualitative Health
Research. Advance online publication. doi:

10.1177/1049732309357435
Tappen, R. M. (2015). Advanced Nursing Research.
[VitalSource Bookshelf]. Retrieved from
https://bookshelf.vitalsource.com/#/books/9781284132496/
van Manen, M. (1990). Researching lived experience: Human
science for an action sensitive pedagogy. London, Ontario:
State University of New York Press. ✧ A classic on
phenomenological research. The focus is on pedagogy
(teaching), but the information is transferable to nursing.
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Page 1 of 2
Rubric Analytical Paper
Excellent
(20–18 points)
Good

(17–16 points)
Competent
(15–14 points)
Weak
(13–12 points)
Inadequate
(11 points or under)
All components
of the analysis
are included
All components of the
analysis are included
and elaborated.
All components are
included but not
elaborated. Or, some are
not elaborated while
others are overwritten.
All components are
included but too little
elaboration .
Some components are
missing.
Most components are
missing.
Understanding of

problem to be
evaluated
Defines problem clearly
and in the very first part
of the analysis.
Defines problem but not
as clear as excellent
answer; may do so later
rather than sooner.
Defines problem but not
clear or, based on
analyses presented, does
not appear to fully
understand the problem.
Undefined problem
causes issues in
determining what and
how to analyze.
Problem is missing or so
vague that it does not
allow a focus for
proceeding; incorrect
understanding of
problem.
Creation of
hypothesis
Clear statement with no
superfluous language;
usually a one-tailed

hypothesis.
Clear hypothesis with
some superfluous
language; frequently a
two-tailed hypothesis;
less clear than an
excellent answer.
States hypothesis in a
minimal way or with
substantial superfluous
language; usually a two-
tailed hypothesis; no
clear understanding of
the problem.
States hypothesis in an
incorrect but still
understandable way;
mostly a two-tailed
hypothesis or if a one-
tailed hypothesis is
phrased in the incorrect
direction to avoid further
elaboration.
Hypothesis is incorrectly
stated and no apparent
understanding of what is
wrong or frequent
attempt to test null
hypothesis or all
hypotheses are phrased
in the incorrect direction
to avoid further

elaboration.
Method The section is written in
a concise and descriptive
so that others could
easily replicate the
method and reproduce
the analysis.
The section is written in
a way that others could
method and reproduce
the analysis; less clear
than an excellent answer
method but not easily
reproduce the results.
not easily replicate the
method or not easily
reproduce the results,
but could come close to
an attempt.
not replicate the method
or not reproduce the
results.

of 2
Excellent
(20–18 points)
Good
(17–16 points)
Competent
(15–14 points)
Weak
(13–12 points)
Inadequate
Selection of
proper analytical
tool
Correct analytical tool
chosen after examination
of data to determine
possible violation of
assumptions and
avoidance of bias.
Appropriate justification
given for choice of tool.
Correct analytical tool
chosen but with little
evidence of examination
for assumption

violations and avoidance
of bias. Less justifi-
cation given for choice
of tool than excellent
answer
Correct analytical tool or
a close match chosen; no
real justification for
choice, little
appreciation of
assumption violations
and/or avoiding bias.
Chosen analytical tool
will approximate one for
a needed answer but no
justification given or
justification is incorrect.
No discussion of bias
and/or assumptions.
Proper analytical tool is
not selected and no
understanding of why
the tool selected is
incorrect.
Results All relevant and
appropriate information
is provided to support
commonly accepted
interpretations and
conclusions.
All relevant and

appropriate information
is provided to support
commonly accepted
interpretations and
conclusions with 1-2
exceptions.
A small amount of
important information
needed for interpretation
is not provided to allow
for commonly accepted
interpretations or
conclusions.
A large amount of
important information
is not provided.
The majority of
necessary information
is not provided.
Interpretation of
results of
applying
analytical tool
Correct interpretation;
explanation of possible
error in accepting
results.
Interpretation correct but

less explanation of
possible error than an
excellent answer.
Interpretation is largely
correct but misses
nuances of the data or
possible error in results.
Interpretation has errors;
no appreciation of
nuances or possible
error.
Interpretation has major
errors or is totally
incorrect and
misstatements are likely.
Discussion of
Results
The discussion carefully
weaves the hypothesis,
literature, interpretation
and implications
together in a meaningful
way.
The discussion weaves
the hypothesis,
and implications
together in a meaningful
way, but less cogently
than an excellent answer

The discussion section
contains the hypothesis,
and implications, but
they are not connected
in a meaningful way
throughtout.
The discussion fails to
consider some of the
following: hypothesis,
literature, interpretation,
or implications.
The discussion fails to
consider most of the
following: hypothesis,
literature, interpretation,
and implications.
Where Score Deductions Can Occur:
1. Not following APA style
2. Not proofreading the paper for composition (e.g., spelling,
grammar, etc.)
3. Too many quotes
4. Missing page numbers
5. Inappropriate in text citations
6. Inappropriate entries in the Reference section
COURSE INFORMATION:
CJ 6321 : Quantitative Analysis in Criminal Justice (CRN:
21932) Semester Credit Hours: 3
PROFESSOR: Dr. Shannon Fowler TERM: Fall 2021
PHONE: 713.223.7996 E-MAIL: [email protected] (preferred)
CLASS HOURS: n/a, an asynchronous online course

OFFICE: CSB 340.J
CLASSROOM: login to the appropriate course in Blackboard
Learn
OFFICE HOURS: Monday 1:00PM - 2:30PM & Wednesday
11:00AM - 12:30PM on Zoom (check Blackboard for meeting
ID and password). Please feel free to contact me. I'm available
by messaging in Blackboard, email, phone, video chat via
Zoom, & face-to-face by appointment (as university allows
C340.J) during other times by appointment.COURSE
DESCRIPTION:
Prerequisite: Graduate standing or department approval, an
undergraduate statistics course within the last 5 years, and CJ
6320.
Description: The use of descriptive and inferential statistics and
computer applications as used in criminal justice
research.COURSE OBJECTIVES:
This course will meet the degree program’s learning objective:
LO 4: Students will be able to interpret and apply techniques of
statistical analysis to the study of crime and justice.
By the end of the course students shall be able to:
Learning Objectives:
Assessed by:
1) Evaluate the design context and data assumptions in order to
appropriately analyze the data
embedded quizzes, projects, class participation, analytical paper
& presentation
2) Analyze data using univariate, bivariate, & multivariate
statistical techniques
& presentation
3) Interpret the results of analyses
embedded quizzes, projects, class participation, analyti cal paper
& presentation
4) Professionally present the results of the analysis
analytical paper & presentation, projects

5) Analyze data using SPSS
embedded quizzes, Introduction to SPSS assignment, projects,
class participation, analytical paper & presentation
6) Choose appropriate statistical techniques for analytical
situations
& presentation
7) Design and execute a data analysis plan for a selected
research question
class participation, analytical paper & presentation,
projectsREQUIRED MATERIALS:
Elliot, A. C., & Woodward, W. A. (2019). Quick guide to IBM
SPSS: Statistical analysis with step-by-step examples (3rd ed.).
Thousand Oaks, CA: Sage. ISBN: 9781544360423
Other required readings and materials will be posted to
Blackboard throughout the course.
IBM SPSS (downloaded from UHD) – Most of your assignments
will require the use of statistical software, SPSS. You can
download it for free from UHD. By clicking this link, you will
complete a license download request from UHD. Once your
student status is verified you will receive further instructions.
Be sure to check your Gatormail for that. You can also use
SPSS for free if you can make it to UHD campus computer labs.
Should the labs become unavailable, students will be required to
obtain the software.
A microphone that is hardwired or adapted to a
computerHIGHLY RECOMMENDED MATERIALS:
Morgan, S. E., Reichert, T., & Harrison, T. R., (2016). From
numbers to words: Reporting statistical results for the social
sciences. New York, NY: Routledge. ISBN: 9781138638082
Williams III, F. P. (2009). Statistical concepts for criminal
justice and criminology. Upper Saddle, NJ: Pearson Education.

ISBN: 978-0-13-513046-9
Copies of the texts can be purchased from the UHD Bookstore
by clicking here.
Book Purchasing. A student of this institution is not under any
obligation to purchase a textbook from a university affiliated
bookstore. The same textbook may also be purchased from an
independent retailer, including an online retailer.FULLY ON-
LINE:
Technology & Network Requirements. This is a fully online
course; as such, all students are expected to have reliable
computing hardware, software, and the Internet & network
access. To maximize your success in online coursework, you
should have access to a desktop or laptop computer running an
up-to-date Windows or macOS operating system, using the
latest Firefox or Chrome browsers. A built-in or add-on webcam
is also often required in certain courses (like this one) where
multimedia tools (Zoom, VoiceThread, etc.) and/or exam
proctoring tools (Lockdown Browser, Monitor, etc.) are used.
Chromebooks and some other tablets are not compatible with
test proctoring tools such as ProctorU or Lockdown Browser.
While the Blackboard App (e.g., on your phone) can be helpful
for some course features, UHD recommends that you do not use
it for working on or submitting graded activities.
To avoid being disconnected at critical moments, we encourage
you to access courses, in particular exams, on a computer that is
hardwired to the Internet router (via Ethernet using a Cat 5, 5e,
6, or 7 cable) as opposed to depending on Wi-Fi whenever
possible. Additionally, this course requires additional software
downloads and installs, so you will need a machine with
permission to do that. For more information on taking
Blackboard tests, see this guide. If you are experiencing
challenges with technology, please communicate with your
instructor in a timely manner and seek help from our UHD IT

support center to identify possible solutions, clicking this link
can get you started.
You should have access to a desktop or laptop computer running
an up-to-date Windows or macOS operating system, using the
latest Firefox or Chrome browsers. To avoid being disconnected
at critical moments, we encourage you to access courses, in
particular exams, on a computer that is hardwired to the Internet
router (via Ethernet using a Cat 5, 5e, 6, or 7 cable) as opposed
to depending on Wi-Fi whenever possible. Additionally, certain
courses may require additional software downloads and installs,
so you may need a machine with permission to do that.
Problems in these areas are not legitimate excuses for failure to
complete assignments or access course materials. These are
important criteria for enrolling and successfully completing an
online course. Making sure these things are in place is the
responsibility of the student. If you do not have access to the
requisite required technology your learning and grades may be
negatively impacted.
You will be required to use Blackboard Learn as part of this
course. This is where the bulk of the course materials are
housed (assignments, lesson notes, etc.). Additionally, this is an
asynchronous online class, students are not required to attend
any specific time-bound events, but will have to complete
assignments and exams within certain timeframes.
If you have a technical problem, please do contact me
immediately for help resolving the issue. Please understand that
I will try to work through as many issues with you as possible;
however, I am not able to “fix” every issue. If you lose access
to Blackboard or have other technology issues please contact
the UHD IT Help Desk. You can contact the Help Desk directly
by phone at (713)221-8031, by chatting at www.uhd.edu/rdm,
by email, or via website.

Additionally, attempting to use the mobile version of
Blackboard may result in a loss of connection, limited access to
content, and unreliable or inadequate submission of
assignments. While the Blackboard App (e.g., on your phone)
can be helpful for some course features, UHD recommends that
you do not use it for working on or submitting graded activities.
I do encourage the use of the BB mobile app to keep up to date
on course notifications.
Online Readiness Self-Assessment.Click here to complete
UHD’s self-assessment to receive specific feedback based on a
student’s individual needs about your readiness to take online
courses. This self-assessment has 22 questions, and it shouldn't
take more than a few minutes for you to complete.
Electronic Communication. The instructor will communicate
with students on an individual basis primarily using official
UHD email. It is the student’s responsibility to check official
UHD email and Blackboard messaging accounts and review the
messages in them. The instructor will communicate with the
class primarily using the announcements feature in Blackboard.
It is the student’s responsibility to read class announcements,
the course question discussion board, etc. for course news.
Additionally, the instructor has a responsibility to respond to
student messages sent to him on a regular basis. I will make
every effort to respond within 2 business days to student-
initiated communications. Emailing me is the best way to reach
me. If you have an emergency and I have not responded to you
call my phone and leave a voicemail stressing the importance of
this situation.COURSE REQUIREMENTS:
Introduction to SPSS. A straightforward assignment that asks
you to create a data set in SPSS. See the Course Outline section
of the syllabus and Blackboard for the due date.
Participation. This is based on small group exercises. Class
attendance and participation is vital to not only demonstrating

comprehension of the course material but also valuable as a
chance for feedback and improvement. Students will often be
placed in small groups (in a way that does not violate UHD
policy on social distancing) to solve problems, execute
analyses, interpret results, and receive feedback that can be
used to improve overall performance. This is a scored portion of
the overall course grade; please do not neglect it.
Lesson notes – embedded quizzes and activities. Most of the
lesson modules will contain embedded quizzes and activities
based on that lesson’s content. By earning at least 80% of the
possible points on these embedded quizzes and activities,
students demonstrate that you have been paying attention to the
material presented. These quizzes and activities do not count
toward students’ final grades. However, in order to access and
complete graded assignments in the lessons or move on to the
next lesson, students must earn at least 80% of the possible
points on these embedded quizzes and activities. As such, these
are ungraded requirements of the course. Students not
completing these quizzes and activities in the lesson notes with
an 80% or better will not be able to complete graded
assignments.
Projects. There will be 11 assignments covering course
materials for that particular lesson. These assignments gauge
students’ understanding & familiarity of the material, their
ability to interpret results, how well they can discuss/present
results, how well they can analyze data with SPSS, and choose
appropriate techniques for data analysis. Each of these
assignments is worth 20 points. A typical project will ask you
to: (a) select variables in one of the available data sets (or will
specify variables and a data set), (b) conduct some form of
statistical analysis presented in the corresponding lesson, and
(c) interpret the results. Sometimes, lesson projects are open-
ended assignments. In other instances, projects are split into
two portions. One portion will ask for closed-ended responses

and short-answer questions. The second portion will be an open-
ended assignment. The dates they are due are listed in the
Course Outline section of the syllabus and must be submitted by
11:59 p.m. CT. Project assignments are late immediately after
the due date.
Analytical Paper & Presentation. The paper and presentation
represent the culmination of the course work. This paper and
presentation should demonstrate the student’s ability to analyze
data over a particular issue in an appropriate and professional
manner. Using data sets available in the class (or outside ones
approved by the professor), students will appropriately analyze
data over an assigned issue. As such, students will ask a
research question, perform a brief literature review, and
generate an appropriate hypothesis. Students will then analyze
data and interpret the results as either supportive or not
supportive of the hypothesis, interpreting other relevant
information as necessary.
In order to check progress and performance, students will make
at least two mandatory appointments to discuss their topics and
progress with the professor at two different points. These
meetings will occur prior to the submission deadline, and earlier
conversations are encouraged. It is up to the student to request
and fit into the professor’s schedule well before the due dates in
the Course Outline. In these meetings students should be ready
to discuss issues encountered with identifying independent and
dependent variables, the suitability of data sources, formulating
a hypothesis, locating relevant literature, data characteristics as
related to appropriate analysis options, and demonstrating both
progress and understanding of the execution and interpretation
of the analysis.
From the material in the paper, students will produce an 8-10
minute professional presentation with visual aids (e.g., slide
deck, Prezi, etc.). This represents a well-prepared oral

description of your paper’s topic and why it is appropriate for
analysis, the hypothesis, method, justification for analytical
plan, results, and conclusions with discussion of (non)support
for the hypothesis and potential sources of error during
interpretation—similar to a presentation at an academic
conference. Expect instructor feedback that may be used to
strengthen the paper prior to submission. Check the Course
Outline for presentation and paper due dates.MAKE-UP
ASSIGNMENTS:
I will not accept late homework or other assignments for credit.
If you know you will miss an assignment or may have trouble
meeting a deadline please contact me ahead of time to work out
arrangements, early submissions for credit are welcome. If there
are any issues with completing any assignment, please contact
the instructor immediately.GRADING SCALE:
Points
Grade
390-351
= A
350-312
= B
311-273
= C
272-234
= D
< 234
= F
Assignment
Points
Projects (11 @ 20 points each)
220
Class Participation
25
Analytical Paper & Presentation
100

Paper Meeting Sign Ups
30
Introduction to SPSS
15
Total
390DROP POLICY:
For more information about information and dates related to
dropping /withdrawing from a course visit the following sites
for a more comprehensive presentation of dropping and
withdrawing from courses.
· Registrar Information About Course Drops
· Financial Aid Information About Course Drops
· Academic Calendar Information About Course
DropsSTUDENT SUCCESS & COURSE PARTICIPATION:
Student Preparation & Success. It is my wish that students
succeed in this class. My role, as an instructor, is to facilitate
students’ learning. Ultimately, however, students have the
responsibility for their education. Students put themselves in
the very best position to succeed by doing, at a minimum, the
following: (1) attend all classes; (2) login to Blackboard and
engage with the course material; (3) be prepared to ask
questions and discuss course material; (4) complete all
assignments on time; and (5) check the BB course site and
course communications several times per week. It is my
expectation that students will spend roughly 4 hours outside
class on your own consuming and reflecting on course
materials, preparing for classes, and completing assignments for
each lesson module.
Course Engagement & Administrative Drop. You must engage
with course materials and/or (depending on your faculty’s
syllabus and course requirements) connect with your faculty
member before the 10th calendar day of class, failure to do so
may result in your being administratively dropped from this
course. Being dropped from this course may affect your
enrollment status and/or your financial aid eligibility. If you are

dropped from this course, you may appeal through the Office of
the Registrar. Once dropped, you will lose access to the course
materials in Blackboard until your appeal is resolved.STUDENT
SUPPORT SERVICES AVAILABLE:
UHD offers students various programs aimed at educational
success. In addition to providing academic advising to all
incoming students, the University College offers support and
tutoring for math at the Math and Statistics Center (phone: 713-
221-8241, email, or appointments via website) and for writing
at the Writing and Reading Center (phone: 713-221-8670, email,
or appointments via website). For further information about
academic support, students can visit the University College’s
website or call (713) 221-8007.Diversity, Inclusion, & Respect:
The material in this course is intended to encourage critical
thinking and some discussion as we examine new ideas and
concepts. As the instructor, I will do my best to foster an
environment in which each class member is able to hear and
respect each other. In turn, it is vital that each class member
show respect for all worldviews and diverse experiences
expressed in class.
It is my intent that students from all diverse backgrounds and
perspectives be well served by this course, that students’
learning needs be addressed both in and out of class, and that
the diversity that students bring to this class be viewed as a
resource, strength and benefit. It is my intent to present
materials and activities that are respectful of diversity in
gender, sexuality, disability, age, socioeconomic status,
ethnicity, race, and culture.
Students are asked to be courteous and considerate at all times.
Rude comments and other disrespectful behavior are
unacceptable. Being considerate also entails not making certain
statements that may be construed as obscene/offensive or using
similar symbols or wording. There are different ways of
accurately and civilly communicating messages. I expect that

each student will take the time to carefully consider and reflect
on their comments before making them. What you say and how
you communicate are important. Keep the following points in
mind when communicating with others in the course:
· Refrain from using judgmental language.
· Avoid victim-blaming language.
· Avoid negativity; frame your comments positively. Feel free
to disagree, but do so in terms of objectively, logically, or
rationally discussing why it is you disagree. Using bold
statements and language that is too frank can stifle
communication and potentially convey that you are not open to
discussion.
· Keep your emotions in check. It is OK to be passionate, but do
not let it overshadow the substance of what you are
communicating—your idea, criticism, or point raised.
· Being humorous or funny may come across in an unintended
fashion; this includes inside jokes. Be straightforward.
· Refrain from using acronyms not everyone would understand.
· Disagree politely.
· Keep comments on track about an idea or issue – avoid
personalizing the issue. Do your best to keep your personal
history and experiences from being the main idea; rather, use
those experiences as jumping off points that other people may
experience or use them as a possible alternative perspective that
others share.
· What you type is important. ALL CAPS can mean you are
screaming! Multiple punctuation marks can be aggressive (????
or !!!!). Make sure what you type is proofread and matches your
intended tone.
Keep in mind that when making statements they could be
subject to subpoena and be made part of the public record.
Communicate accordingly.Preparing for Emergencies:
I encourage each student to have a backup plan in case
emergency circumstances arise while you are enrolled in this
course (e.g., computer crash, natural disaster, medical
emergency, etc.). I recommend the following:

· Rely on someone you trust to contact the instructor in an
emergency. In case you are unable to contact me personally,
give someone you trust my contact information and instruct
them to contact me on your behalf should you not be able to do
so, to briefly describe what the general nature of your
emergency is and why you will not be able to complete the
course or assignments.
· Save your work and maintain back-ups that are easily
accessible to you. For instance, you could save copies of your
work to a flash drive or cloud storage (e.g., Dropbox, Google
Drive, Microsoft OneDrive, etc.). When your primary computer
crashes, be sure to have backup copies to minimize your losses.
· Locate and make sure you can access a secondary computing
source. In case you lose your primary computing source that
you rely on to complete assignments, be sure to plan ahead and
locate alternate computers or devices you can use to complete
the necessary work in this course. Examples could be making a
trip to a UHD campus computer lab, visiting your local library,
or rely on someone you trust to help you as needed.
· Locate and make sure you can access a secondary Internet
source. Have a plan on how you will access the Internet to
complete and submit your coursework if your primary source
becomes unavailable. For instance, if you live near UHD’s
campus you can use your credentials to log on to computers in
the campus labs, visit your local library, go to your favorite
coffee shop, find guest access from a public school, or rely on
someone you trust to help you as needed.UHD Common Course
Syllabus Policies
Responses to University-Wide Disruptions. In the event of
university-wide disruptions for any reason, including weather,
health, and safety concerns, UHD may require instructors and
students to engage in their classes via different modalities
and/or timelines to minimize disruption to the continuity of the
semester. Such changes may entail adjustments in syllabus
content. Instructors will communicate any changes in writing to
all enrolled students as soon as circumstances allow.

Disruptions aside, instructors reserve the right to adjust their
syllabi as needed in order to accommodate the education needs
of the class, but any such changes will be communicated to
students in writing during the course of the semester. Please
continue to check the UHD website uhd.edu to understand how
UHD is responding to the most current COVID-19
circumstances and regularly check your class Blackboard site
and Gatormail sources for information specific to your classes.
COVID-19 Exposure or Diagnosis. Any student who is exposed
to or diagnosed with COVID-19 should self-report to the
university using forms found on our UHD COVID-19 Webpage,
even if you are taking only online courses. Self-reporting allows
the university to offer support and guide you to university and
community resources, as well as maximize safety for the larger
UHD community. You as a student may be eligible for short-
term academic accommodations if you are affected by COVID
or are asked to quarantine. You should make this request
through the Office of Disability Services. Please note that
reporting through faculty or staff does not constitute official
self-reporting. For classes with in-person meetings (FTF or
Hybrid): If any member of the class reports exposure or
diagnosis while on campus, instructors will follow the
instructions for immediate action posted in all classrooms. Our
UHD contact tracing team may contact class members and
instructor with appropriate steps, which may include self-
isolation for a period of time. All students and instructors
should be responsive to contact tracing outreach and watch for
emails through official UHD email accounts for any information
about class meetings or follow-up steps.
Safety Precautions. All individuals coming to the UHD campus
must observe all safety precautions articulated by the
university. Please review the most current requirements on our
website. We encourage all UHD community members to get
vaccinated and follow; masks are optional but encourages as per

state health guidance. Failure to comply with any institutional
policies, including those regarding COVID precautions, may
constitute a violation of the student code of conduct and lead to
disciplinary action through the Office of the Dean of Students.
Student Counseling Services. As a student you may experience a
range of issues that can cause barriers to learning. These might
include strained relationships, anxiety, high levels of stress,
alcohol/drug problems, feeling down, or loss of motivation.
UHD Student Counseling Services is here to help with these or
other issues you may experience. You can learn about the free,
confidential mental health services available on campus by
calling 713-500-3852 or at https://www.uhd.edu/student-
life/counseling/.
Accessibility and Statement of Reasonable Accommodations.
The University of Houston-Downtown (UHD), is committed to
creating a learning environment that meets the needs of its
diverse student population. Accordingly, UHD strives to
provide reasonable academic accommodations to students who
request and are eligible, as specified by Section 504 and ADA
guidelines. Students with disabilities may work with the Office
of Disability Services to discuss a range of options to removing
barriers in this course, including official accommodations. If
you have a disability, or think you may have a disability, please
contact the Office of Disability Services, to begin this
conversation or request an official accommodation. Office of
Disability Services, One Main St., Suite GSB 314, Houston, TX
77002. (Office Phone) 713-221-5078 (Website)
www.uhd.edu/disability/ (Email) [email protected]
Technology Requirements. All classes at UHD require students
to access materials in our Blackboard learning system or other
learning applications. Online, hybrid or even face-to-face
classes will assign work that requires access to a computer for
creating and submitting assignments, taking tests, conducting

research, working with classmates, or engaging with the class.
As importantly, if University locations are not available to
students for any reason, the online environment becomes a
critical pathway for continuing our classes and supporting your
goals of completion. Unfortunately, most phones and even some
tablets may not provide the level of technology or access that
can maximize your success. Therefore, it is essential for every
student at UHD to have reliable access to internet and a
computer that meets some basic requirements.
You should communicate in a timely manner with your
instructors in the case of any challenges in using technology.
Here are some resources to help you determine equipment needs
and usage:
For recommended technology requirements: Technology
recommendation
For challenges in using technology: UHD IT support center
For resources on purchasing technology: Computer access and
support
Testing and Final Exams. Any class may use an online testing
option through Blackboard but for in-person or hybrid classes,
the exam may be in-person during the scheduled exam period.
For more information on taking Blackboard tests, see this guide.
If proctoring is required, your instructor will inform you of the
process for setting up this option either through Blackboard or
an alternative venue, and they will inform you of whether there
are any additional costs as part of the course syllabus. UHD has
a final exam period at the end of the semester. For any courses
with an in-person component, there are specific times scheduled
for the exams which can be found on our academic calendars
webpage. Students are expected to be available during the
scheduled period unless they have consulted their instructor and
identified an alternative option.

Use of Blackboard, Gatormail, and Zoom. You are expected to
regularly participate in your classes as scheduled as wel l as
engage course material through Blackboard as required by
instructors. Gatormail is the official UHD email communication
system and UHD staff and faculty must use it to share student-
specific information that is protected by FERPA guidelines.
You should check your account regularly for both class and
university messages. If you are taking a class that has virtual
online meetings that use Zoom, you are expected to attend at
scheduled times and participate fully following any protocols
established by your instructor. Specific course elements and/or
exams may require live video. Your instructor will provide this
information to you as part of the course syllabus. Students with
concerns regarding any requirement to participate in live video
for specific course learning outcomes and/or assignments should
consult their instructor.
Recording of Class Sessions. Some of the sessions in courses
with online engagement may be pre-recorded, recorded or live-
streamed by the instructor. Such recordings/streaming will be
available only to students registered for this class. Students
should not share these instructor-recorded sessions with those
not in the class, or upload them to any other online
environment. Students should not record or stream course
sessions. Doing so may be a violation of the Federal Education
Rights and Privacy Act (FERPA). Please check with your
instructor before sharing recordings of class content with any
individual.
Academic Honesty. As a UHD student, you are responsible for
following the UHD Academic Honesty Policy Statement 3.A.19,
which defines the scope of academic honesty and identifies
processes for addressing violations, including an appeal
process. As per the policy, “students are responsible for
maintaining the academic integrity of the University by
following the Academic Honesty Policy. Students are

responsible for doing their own work and avoiding all forms of
academic dishonesty.” Academic dishonesty includes, but is not
limited to, cheating and plagiarism. Your faculty member will
identify the penalty for academic honesty violations and the
penalty of an F in a course is recommended “in instances of
multiple and/or flagrant violations.” The policy also requires
that all violations are reported to the Office of the Dean of
Students.OTHER POLICIES:
Visiting Students. If you are a student visiting from another
institution taking this class, you are bound by policies at UHD
and those outlined in (and for) this course. Additionally, you
may still have to abide by policies from your home institution.
Syllabus Changes. The schedule, policies, and assignments in
this course are subject to change at the instructor’s discretion. I
will make a concerted effort to adhere to the all things in the
syllabus, but unforeseen circumstances sometimes arise that
require altering both content, practices, and schedules.COURSE
OUTLINE:
Class Date
Lesson
Assigned Readings
Assignments
8/23
Introduction to Statistics: (1) Types & purposes of statistics, (2)
Research design, (3) Variability & measurement, (4) SPSS
overview
Elliott: pp. 1-6; 10-13, Appendix B
- - - - - - - - - - - - - -
Williams: Ch. 1
Orientation Quiz
Due: 8/29
8/30
Levels of Measurement & Using SPSS: (1) Levels of
measurement, (2) Working with SPSS data
Elliott: pp. 13-23 & Appendix A

- - - - - - - - - - - - - -
Williams: Ch. 2
Introduction to SPSS
Due: 9/5
Paper Topic Sign-Up
Due: 9/5
9/6
Descriptive statistics-graphs, tables, & figures: (1) Types of
graphs, (2) Frequency distributions, (3) Using SPSS to generate
graphs & frequency distributions
Elliott: pp. 42-51 &
Ch. 3
- - - - - - - - - - - - - -
Williams: Chs. 3 & 7, Appendix A
Project 1
Due: 9/12
Sign up for 1st paper meeting by 9/12
9/13
Descriptive Statistics-summary measures: (1) Measures of
central tendency, (2) Measures of dispersion, (3) Describing
distributions, (4) Properties of distributions
Review Elliot Ch. 3
- - - - - - - - - - - - - -
Williams: Chs. 4-6, Appendix B
Project 2
Due: 9/19
Sign up for 2nd paper meeting by 9/19
9/20
Describing bivariate data & hypotheses: (1) Cross-tabulations,
(2) Comparing group means, (3) Scatterplots, (4) Logic of
hypothesis testing, (5) Creating hypotheses, (6) Significance
Elliott: pp. 6-10; 51-56

- - - - - - - - - - - - - -
Williams: Chs. 8 – 10
Project 3
Due: 9/26
9/27
Chi-square & variants: (1) Chi-square tests, (2) Alternatives to
the chi-square test (Fischer’s Exact test, Maximum likelihood
chi-square), (3) Layering contingency tables
Elliot: pp. 167-187
- - - - - - - - - - - - - -
Williams: Ch. 11, pp. 160-162
Project 4
Due: 10/3
10/4
Measures of association, part 1: (1) Association & correlation,
(2) Proportionate reduction of error, (3) Phi-coefficient, (4)
Lambda, (5) Uncertainty coefficient
Elliot: pp. 211-212
- - - - - - - - - - - - - -
Williams: Chs. 14 & 15, Appendix E
Project 5
Due: 10/10
10/11
Measures of association, part 2: (1) Gamma, (2) Somer’s d, (3)
Pearson’s r
Elliot: pp. 125-148
- - - - - - - - - - - - - -
Williams: PP. 147-154 & Appendix F
Project 6
Due: 10/17
10/18
Group comparisons, part 1: (1) Mann-Whitney U, (2) Kruskal-
Wallis H, (3) t-tests (independent-samples & related-samples)
Elliot: Ch. 4 & pp. 277-285
- - - - - - - - - - - - - -
Williams: Appendix C & Ch. 12

Project 7
Due: 10/24
10/25
Group comparisons, part 2: (1) One-way ANOVA, (2) two-way
ANOVA
Elliot: pp. 215-249
- - - - - - - - - - - - - -
Williams: Ch. 13 & Appendix D
Project 8
Due: 10/31
11/1
Multivariate tests for continuous outcomes: (1) Partial
correlation, (2) Semi-partial correlation, (3) Multivariate
[multiple] regression
Elliot: pp. 137-166
- - - - - - - - - - - - - -
Williams: pp. 154-168
Project 9
Due: 11/7
11/8
Multivariate test for binary outcomes: (1) binary logistic
regression
Elliot: Ch. 9
Electronic handout
Project 10
Due: 11/14
11/15
Multivariate test for count outcomes: (1) Poisson regressi on, (2)
Negative binomial regression
Electronic handout
Project 11
Due: 11/21
12/5
Analytical Paper Presentations Due by 11:59 p.m.
12/14
Analytical Papers Due by 11:59 p.m.

Fowler CJ 6321 Spring 2021 1 of 10
Page 2 of 2
Page 2 of 2
Excellent
(20–18 points)
Good
(17–16 points)
Competent
(15–14 points)
Weak
(13–12 points)
Inadequate
All components of the analysis are included
All components of the analysis are included and elaborated.
All components are included but not elaborated. Or, some are
not elaborated while others are overwritten.
All components are included but too little elaboration .
Some components are missing.
Most components are missing.
Understanding of problem to be evaluated
Defines problem clearly and in the very first part of the
analysis.
Defines problem but not as clear as excellent answer; may do so
later rather than sooner.
Defines problem but not clear or, based on analyses presented,
does not appear to fully understand the problem.
Undefined problem causes issues in determining what and how
to analyze.

Problem is missing or so vague that it does not allow a focus for
proceeding; incorrect understanding of problem.
Creation of hypothesis
Clear statement with no superfluous language; usually a one-
tailed hypothesis.
Clear hypothesis with some superfluous language; frequently a
two-tailed hypothesis; less clear than an excellent answer.
States hypothesis in a minimal way or with substantial
superfluous language; usually a two-tailed hypothesis; no clear
understanding of the problem.
States hypothesis in an incorrect but still understandable way;
mostly a two-tailed hypothesis or if a one-tailed hypothesis is
phrased in the incorrect direction to avoid further elaboration.
Hypothesis is incorrectly stated and no apparent understanding
of what is wrong or frequent attempt to test null hypothesis or
all hypotheses are phrased in the incorrect direction to avoid
further elaboration.
Method
The section is written in a concise and descriptive so that others
could easily replicate the method and reproduce the analysis.
The section is written in a way that others could easily replicate
the method and reproduce the analysis; less clear than an
excellent answer
The section is written in a way that others could easily replicate
the method but not easily reproduce the results.
The section is written in a way that others could not easily
replicate the method or not easily reproduce the results, but
could come close to an attempt.
The section is written in a way that others could not replicate
the method or not reproduce the results.
Selection of proper analytical tool
Correct analytical tool chosen after examination of data to
determine possible violation of assumptions and avoidance of
bias. Appropriate justification given for choice of tool.
Correct analytical tool chosen but with little evidence of
examination for assumption violations and avoidance of bias.

Less justifi-cation given for choice of tool than excellent
answer
Correct analytical tool or a close match chosen; no real
justification for choice, little appreciation of assumption
violations and/or avoiding bias.
Chosen analytical tool will approximate one for a needed
answer but no justification given or justification is incorrect.
No discussion of bias and/or assumptions.
Proper analytical tool is not selected and no understanding of
why the tool selected is incorrect.
Results
All relevant and appropriate information is provided to support
commonly accepted interpretations and conclusions.
All relevant and appropriate information is provided to support
commonly accepted interpretations and conclusions with 1-2
exceptions.
A small amount of important information needed for
interpretation is not provided to allow for commonly accepted
interpretations or conclusions.
A large amount of important information needed for
interpretation is not provided.
The majority of necessary information needed for interpretation
is not provided.
Interpretation of results of applying analytical tool
Correct interpretation; explanation of possible error in
accepting results.
Interpretation correct but less explanation of possible error than
an excellent answer.
Interpretation is largely correct but misses nuances of the data
or possible error in results.
Interpretation has errors; no appreciation of nuances or possible
error.
Interpretation has major errors or is totally incorrect and
misstatements are likely.
Discussion of Results
The discussion carefully weaves the hypothesis, literature,

interpretation and implications together in a meaningful way.
The discussion weaves the hypothesis, literature, interpretation
and implications together in a meaningful way, but less
cogently than an excellent answer
The discussion section contains the hypothesis, literature,
interpretation and implications, but they are not connected in a
meaningful way throughtout.
The discussion fails to consider some of the following:
hypothesis, literature, interpretation, or implications.
The discussion fails to consider most of the following:
hypothesis, literature, interpretation, and implications.
Where Score Deductions Can Occur:
1. Not following APA style
2. Not proofreading the paper for composition (e.g., spelling,
grammar, etc.)
3. Too many quotes
4. Missing page numbers
5. Inappropriate in text citations
6. Inappropriate entries in the Reference section

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