this activity is designed for you to explore the continuum of an addictive behavior of your choice.  Addictive behavior appears in stages. The earliest stage is non-use, which finally leads up to out-of-control dependence. The stages in between are important to identify, as it is much easier to correct an early-stage issue as opposed to a late-stage problem. After reviewing the module readings and tasks, use the module notes as a reference and alcohol or substance abuse addiction as an example to identify the various levels of addiction. You may choose to develop a time line identifying the stages or develop a written essay (no more than 500 words in Word format) to describe the escalation of addictive behaviors. You are to include at least two references from academic sources that you have researched on this topic in the Excelsior College Library and use appropriate citations in American Psychological Association (APA) style. You cannot just do a Google search for the topic! Academic sources are required. You may use Google Scholar or other libraries. Chapter 13 Qualitative Data Analysis 1 Process of Qualitative Data Analysis Preparing the Qualitative Data Transform the data into readable text Check for and resolve transcription errors Manage the data Organize by attribute coding Two Separate Processes 5 Coding: Involves labeling and breaking down the data to find: Patterns Themes Interpretation: Giving meaning to the identified patterns and themes Coding Starts with identifying the unit of analysis Coding categories may reflect realms of meaning or different activities. Coding categories can be theoretically-based or inductively created emerging from the data. Use of Analytical Memos 7 Analytical memos help researchers w/ process of breaking down the data Personal reflections on the research experience, methodological issues, or patterns in the data Comes in 3 varieties: Code notes Operational notes Theoretical notes Data Displays Taxonomy: system of ordered classification Data matrix: individuals or other units represent columns and coding categories represent rows Typologies: representation of findings based on the interrelationship between two or more ideas, concepts, or variables Flow charts: diagrams that display processes Taxonomy of Survival Strategies Data Matrix: Homeless Individuals by Dimensions Drawing and Evaluating Conclusions Conclusions may result in: Rich descriptions Identification of themes Inferences about patterns and concepts Theoretical propositions Evaluation of the data can occur by: Comparing notes among observers Using multiple sources of data Examining exceptions to the data patterns Member checking Variations in Qualitative Data Analysis: Grounded Theory Objective is to develop theory from data Emphasizes people’s actions and voices as the main sources of d.

1. this activity is designed for you to explore the continuum of an
addictive behavior of your choice.
Addictive behavior appears in stages. The earliest stage is non-
use, which finally leads up to out-of-control dependence. The
stages in between are important to identify, as it is much easier
to correct an early-stage issue as opposed to a late-stage
problem.
After reviewing the module readings and tasks, use the module
notes as a reference and alcohol or substance abuse addiction as
an example to identify the various levels of addiction.
You may choose to develop a time line identifying the stages or
develop a written essay (no more than 500 words in Word
format) to describe the escalation of addictive behaviors.
You are to include at least two references from academic
sources that you have researched on this topic in the Excelsior
College Library and use appropriate citations in American
Psychological Association (APA) style.
You cannot just do a Google search for the
topic! Academic sources are required. You may use Google
Scholar or other libraries.
Chapter 13
Qualitative Data Analysis
1

2. Process of Qualitative Data Analysis
Preparing the Qualitative Data
Transform the data into readable text
Check for and resolve transcription errors
Manage the data
Organize by attribute coding

3. Two Separate Processes
5
Coding: Involves labeling and breaking down the data to find:
Patterns
Themes
Interpretation: Giving meaning to the identified patterns and
themes
Coding
Starts with identifying the unit of analysis
Coding categories may reflect realms of meaning or different
activities.
Coding categories can be theoretically-based or inductively

4. created emerging from the data.
Use of Analytical Memos
7
Analytical memos help researchers w/ process of breaking down
the data
Personal reflections on the research experience, methodological
issues, or patterns in the data
Comes in 3 varieties:
Code notes
Operational notes
Theoretical notes
Data Displays

5. Taxonomy: system of ordered classification
Data matrix: individuals or other units represent columns and
coding categories represent rows
Typologies: representation of findings based on the
interrelationship between two or more ideas, concepts, or
variables
Flow charts: diagrams that display processes
Taxonomy of Survival Strategies

6. Data Matrix: Homeless Individuals by Dimensions
Drawing and Evaluating Conclusions
Conclusions may result in:
Rich descriptions
Identification of themes
Inferences about patterns and concepts
Theoretical propositions
Evaluation of the data can occur by:
Comparing notes among observers
Using multiple sources of data
Examining exceptions to the data patterns

7. Member checking
Variations in Qualitative Data Analysis: Grounded Theory
Objective is to develop theory from data
Emphasizes people’s actions and voices as the main sources of
data
Uses constant-comparative method to analyze the data
Data collection, coding, and interpretation are closely integrated
in CCM
Variations in Qualitative Data Analysis: Grounded Theory
Involves taking multiple passes through the data:
Pass 1: No coding is done
Pass 2: Open coding
Pass 3: Axial coding

8. Pass 4: Theoretical coding
Variations in Qualitative Data Analysis:
Thematic Analysis
Main goal is to identify themes expressed within a text.
Criteria for identifying themes:
Recurrence
Repetition
Forcefulness
Variations in Qualitative Data Analysis
Narrative analysis
Objective is to examine the structure, meaning, and other
characteristics of stories

9. Used to analyze journals, interviews, and other personal sharing
of data
Used in conjunction with life history interviews
Conversation analysis
Objective is to analyze the structure, sequencing, word choice,
and other characteristics of conversations
May be based on video and/or audio recordings
Uses a detailed shorthand notation to indicate speakers’ pauses,
emphases, silences
.MsftOfcThm_Accent1_Fill {
fill:#B01513;
}
.MsftOfcThm_Accent1_Stroke {
stroke:#B01513;
}
Chapter 12

10. Quantitative Data Analysis: Part 3
Testing Hypotheses of Differences
Hypotheses of differences focus on predicting differences in the
dependent variable across different levels of an independent
variable.
There are 3 options available for testing differences across
groups:
Chi-square
t-test
Analysis of variance (ANOVA)
Chi-Square
Examines whether frequencies found for different levels of the
dependent variable is influenced by different levels of the
independent variable.

11. Chi-square looks at comparing observed frequencies to expected
frequencies.
Chi-square statistic can be calculated by using cross-tabulations
or contingency tables.
Rows represent the dependent variable
Columns represent the independent variable
Attitude Toward Gun Control by Sex:
Frequency Table
Measures of Association For Nominal or Ordinal-Level
Variables
To determine how strongly frequencies are related between 2

12. nominal or ordinal-level variables, a variety of measures can be
used:
Percentage differences
Cramer’s phi coefficient
Chi-square statistic
The chi-square statistic requires that both the IV and the DV are
at the nominal or ordinal-level of measurement.
t-test
Examines whether mean differences in the dependent variable
across 2 groups is influenced by the independent variable.
Compares observed mean differences between 2 groups to
expected mean differences based on chance.
The t-test is used only when the following are met:
The IV is a nominal-level variable with 2 categories or levels

13. The DV is an interval or ratio-level variable
t-test
There are 4 types of t-tests available depending on 2 factors
Independent vs. paired samples
One-tailed vs. two-tailed test
Analysis of Variance (ANOVA)
An ANOVA allows us to determine whether mean differences in
the dependent variable across 3 or more groups is influenced by
the independent variable.
Types of ANOVAs used depend on specific conditions:
One-way ANOVA
Factorial ANOVA
MANOVA
Additional variations of the ANOVA:
ANCOVA

14. Repeated-measures ANOVA
Analysis of Variance (ANOVA)
The F statistic (or F-ratio) is calculated to determine whether 3
or more group means significantly differ from each other in
terms of the dependent variable.
The F-ratio compares the average variance between the groups
examined to the average variance within each group.
Average variance between groups is known as mean square
between (MSB).
Average variance within each group is known as mean square
within (MSW).
Determining Significance in Hypothesis Testing
When testing hypotheses of covariation or hypotheses of

15. differences, significance is determined by comparing the
calculated inferential statistic to the critical inferential statistic
value found in a table.
The chi-square, t-statistic, and F-statistic each have their own
tables of critical values based on a normal distribution of data.
A hypothesis is supported if the calculated statistic is greater
than the critical value.
A hypothesis is not supported if the calculated statistic is lesser
than or equal to the critical value.
Chapter 12
Quantitative Data Analysis: Part 2
Logic of Hypothesis Testing
Calculating inferential statistics allow us to test hypotheses to
determine:
If 2 or more variables have linear relationships (hypothesis of
covariation)

16. If 2 or more groups differ on an outcome variable (hypothesis of
difference)
Statistical tests examine the probability that an observed linear
relationship between variables or differences between groups
occurred by chance
Errors in Hypothesis Testing
Null should not be rejected in realityNull should be rejected in
realityDecide to reject the null based on testType I error – Null
is rejected even though it should not beDecision 1 – Null is
rejected when it should beDecide not to reject the null based on
testDecision 2 – Null is not rejected when it should not beType
II error – Null is not rejected even though it should be
Testing Hypotheses of Covariations
4
Hypotheses of covariation focus on predicting linear
relationships between two or more interval/ratio-level variables.

17. There are 2 options available for testing linear relationships:
Regression
Both tests are similar in their abilities to test for a linear
relationship between variables, but not a curvilinear
relationship.
Key difference is in interpretation of the 2 variables examined.
Correlation – either variable can be the IV or DV
Regression – there is a clear indicator of the IV and the DV
Correlation
Correlations
5

18. Correlation coefficient r has values ranging from -1.00 to 1.00
Number provides an indicator of strength of the linear
relationship
Sign provides direction of relationship
Four common types:
Pearson correlation
Point-biserial correlation
Phi correlation
Spearman rho correlation
Interpreting the Coefficient
Direction
of relationship
Positive- both variables increase or both variables decrease
Negative – one variable increases while the other decreases
Relationship strength
< .20 – slight, almost negligible

19. .20-.40 – low, definite but small
.40-.70 – moderate, substantial
.70-.90 – high; marked
>.90 – very high or dependable
6
Regressions
Estimates the linear relationships between one or more
independent variable(s) and a dependent variable.
Regression analysis focuses on finding the best fitting straight
line to predict the dependent variable based on the data.
General formula for regression line: Y = a + bX
a is the Y-intercept
b is the slope/regression coefficient:

20. Scatterplot: Cumulative GPA and Semester GPA
Scatterplot: Cumulative GPA and Number of Drinks
Regressions
Regression coefficient indicates magnitude of the effect
There are 2 basic types of regressions:
Simple linear regression

21. Multiple regression
Other Forms of Regression
11
Hierarchical regression Researcher enters IVs in the order in
which they are theoretically presumed to influence the DV.
Stepwise regression
Order of variables is determined by statistical analysis based on
the degree of influence each IV has on the DV.
Beta Weights
12

22. Also known as beta coefficients (β)
Provides a standardized measure of the magnitude of influence
for different IVs on the DV.
Coefficients range from +1.00 to –1.00
Chapter 12
Quantitative Data Analysis: Part 1
Process of Quantitative Data Analysis
Steps in Data Processing
Coding

23. Transforming data into numbers
Entering
Input data into a data file with rows for cases and columns for
variables
Cleaning
Detecting and resolving errors in the data file
Verification of data entries
Wild-code and consistency checking
Data Inspection and Modification
Look for extreme values or outliers in your data.

24. Prevalence of missing values
Listwise deletion
Imputation
How data may be modified by recoding and by combining two
or more variables
Descriptive and Inferential Statistics
Descriptive statistics
Help us organize and summarize data with the goal of making
them more intelligible.
Inferential statistics
Help us to estimate population characteristics based on sample
data and test hypotheses.
Calculating Descriptive Statistics

25. Involves producing frequency and percentage distributions
Involves calculating a variety of univariate statistics
Measures of central tendency
Measures of dispersion
Measures of Central Tendency
Mean = It is an unbiased estimate of the population mean.
Median = It is the 50th percentile value in an ordered
distribution
Mode = Most frequently occurring score
Measures of Variability or Dispersion

26. Range = Difference between the highest and lowest values in a
distribution of values
Variance = average deviation of scores from the mean
Standard deviation = standardized average deviation from the
mean.
Reflects the shape of the distribution for a set of data
Describing Data Distributions
Data distributions can broadly be described as having a
Normal distribution
Skewed distribution
Mean > median or mode (positive)
Mean < median or mode (negative)

27. Data distributions can also be described based on height
(kurtosis value):
Platykurtic
Mesokurtic
Leptokurtic
Normal Distribution and Standard Deviation
10
Concluding Thoughts on Descriptive Statistics
Type of descriptive statistics calculated for a given variable
depends on the level of measurement for the variable.

28. Nominal or ordinal level of measurement
Interval or ratio level of measurement
Descriptive statistics can be summarized visually using a
variety of formats such as:
Tables
Graphs (Histogram, pie chart)
Study Aid for COMM 3023 Exam 3
Exam 3 will cover materials from Chapters 11-14.
Chapter 11 – Multiple Methods
1. What 2 strengths do experiments provide that are weaknesses
of field research?
2. What 2 strengths are shared by experiments and surveys?
3. What are the 4 purposes of doing mixed methods research?
4. Know the 2 dimensions that allows us to describe mixed
methods research designs.
5. What is the difference of taking a fixed vs. random effects
model for doing a meta-analysis?
Chapter 12 – Quantitative Data Analysis
6. Know what it means to carry out dummy coding, wild-code
checking, and listwise deletion.
7. When entering data in a matrix table, what do the rows and

29. columns represent?
8. What measure of central tendency provides us with a true
central value?
9. What measures of central tendency & variability are
negatively impacted by outliers?
10. What is the definition of variance?
11. How is standard deviation related to the kurtosis for a
distribution of scores?
12. What can the measures of central tendency tell us about the
skewness for a data set?
13. Distinguish between a positively and a negatively skewed
distribution of scores.
14. What % of scores falls within +/- 1 interval of standard
deviation for a perfect curve?
15. Percentage distributions are useful for describing data at
what level(s) of measurement?
16. How should we interpret the data when p<.05 when
comparing 2 groups on an outcome?
17. What is the difference between Type 1 and Type II error?
18. What is the link between significance level & the 2 types of
hypothesis testing errors?
19. What key similarity is shared by correlations and
regressions?
20. Know the different types of correlations available and when
are they used.
21. Know the general guidelines for interpreting the strength of
the correlation coefficient.
22. The general regression line formula is Y=a+bx. Know what
a and b represent.
23. What is the difference between a simple linear vs. multiple
regression?
24. What are the two special forms of regressions and when are
they used?
25. What do beta weights (or beta coefficients) provide us a
measure of?
26. When should chi-square be used?

30. 27. When should a t-test be used?
28. What is the difference between an independent-samples and
a paired-samples t-test?
29. What is the difference between a one-tailed vs. a two-tailed
t-test?
30. What is the standard error?
31. When should an ANOVA be used?
32. What is the difference between a one-way ANOVA,
factorial ANOVA, and MANOVA?
33. What are the two variations of the regular ANOVA and
when are they used?
34. What does the F-ratio compare?
35. What basic decision rule applies in deciding whether or not
a hypothesis is supported?
Chapter 13 – Qualitative Data Analysis
36. What 3 steps are involved in the feedback loop of
qualitative data analysis?
37. What are the 2 common types of error that occur during
transcription of data?
38. What are the 3 different types of analytical memos?
39. What are the 4 types of data displays used for qualitative
data?
40. What are the 4 ways for evaluating conclusions drawn
based on qualitative data?
41. What does the constant comparative method entail within
grounded theory?
42. What are the 3 types of coding used in grounded theory &

31. when are they performed?
43. What are the 3 criteria needed to help identify themes
within a thematic analysis?
44. What type of analysis could be used in conjunction with
life history interviews?
Chapter 14 – Reading and Writing Research Reports
45. What are the 3 key steps prior to designing and preparing a
research proposal?
46. What part of an article should be reviewed to judge its
relevance for a literature review?
47. What does the practice of free writing refer to?
48. What part of research report addresses the question, “Why
should I care?”
49. What are the different parts that make up the methods
section of a research report?
50. What steps should be taken to ensure the final report is of
high quality?