this activity is designed for you to explore the continuum of an a.docx
- 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?