1. Class 10 Jeff Driskell, MSW, PhD
Research for Social Workers Salem State University School of Social Work
Class 10
Jeff Driskell, MSW, PhD

2. Today’s Class Announcements/Check-in Lecture Work time Data Analysis
Frequencies
Descriptive Statistics
Measures of central tendency
Work time

3. Reflection- Class 10 Article Reflection
What are the indicators that lead you to believe your article is qualitative in nature (other than it saying so)?
What type of method selected (i.e. grounded theory)
Hypothetically the study you are proposing is qualitative in nature. Identify three types of rigor you would employ to increase trustworthiness.

4. Statistics and Interpretation
Statistics and Interpretation

5. Statistics Holistic Approach- It provides tools for-
collection, analysis, interpretation/ explanation, and presentation of data.
It provides tools for-
description, prediction, and relationship identification
Levels- What are the levels at which analysis are conducted?
Univariate- Statistics that describe one variable at a time
Bivariate- Statistics that looks at the relationship between two variables.
Multivariate- Statistics that looks at the relationship between multiple variables

6. Ways to Organize and Present Data
Tables
Descriptive tables
Frequency tables
Graphs
Bar
Histogram
Narrative
Matrix
Correlation

7. Statistical Analysis- What you need to know
Purpose of test
Match question to test
Logistics for test
Structure of variable needed for test
Continuous
Nominal
Number of variables needed for test
Interpret test findings
Basic knowledge of the “squigglies” in reporting statistical results

8. Review: Variable structure
Provide an example of a continuous level variable
Provide an example of a discrete level variable

9. Types and Categories of Statistical Tests

10. 4 basic categories of statistical tests
Mean, standard deviation
Median, Mode
Percentage, frequency
Description
Pearson’s correlation
Correlation
Student’s t tests
Chi-square tests
ANOVA
Odds ratios
Comparison
OLS regression
Logistic regression
Prediction

11. Statistical Tests: Implications of Normal Distributions
Two categories of statistical tests
1. Parametric
Used for normally distributed data
Example- T-test, ANOVA
Correlation Coefficient
2. Non-parametric
Used for non-normally distributed data
Mann-Whitney
Wilcoxon’s matched tests
Chi Square
Kendall
Spearman R

12. If You Want to Describe or Summarize Variables…

13. Univariate & Descriptive
Univariate - describe individual variables
Descriptive – describe, summarize data
Frequency
Range
Percentage
Central tendency (simple mean, median mode)
Standard deviation

14. Frequencies
A Frequency (f) is a count of the number of cases or characteristics of selected cases, i.e. the number of treatment sessions attended.

15. Frequency Distributions
Two ways to display Frequency Distributions
1. Frequency tables
2. Histograms/Bar charts/Pie Graphs

16. Where do the Numbers Come From? Frequency Output in SPSS

17. Example- Frequency Table
Frequency Distribution: Number of Children Reported by Parenting Class Participants
Number of Children
Frequency (f) 1 11 2 3 4 5 6
Total= 35

18. Example- Bar Graph

19. Histogram

20. Descriptive

21. Descriptive Statistics
Statistical procedures used to summarise, organize, and simplify data.
Raw data is made more manageable
Raw data is presented in a logical form
Patterns can be seen from organized data
Frequency tables
Graphical techniques
Measures of Central Tendency
Measures of Spread (variability)

22. Measures of Central Tendency
A way of summarising the data using a single value that is in some way is representative of the entire data set. Three options…
1. Mode
Most frequent value occurring in your data (frequency)
Unaffected by extreme scores (outliers)
Not useful when there are several values that occur equally often in a set. However can be more than one mode
Can be measured on any level

23. Measures of Central Tendency
2. Median
The values that falls exactly in the midpoint of a ranked distribution (50th percentile)
Unaffected by extreme scores (outliers)
3. Mean (Arithmetic average)
Average score
Preferred measure; Most commonly reported measure
Only for continuous variables (ratio, interval)
Easily distorted by extreme values (outliers)

24. Standard deviation
Most commonly used measure of dispersion around a mean
how “spread out” are the values?
Always reported with the mean – otherwise considered to be biased
Can’t be done with nominal variables

25. Socio-behavioral model
Table 1: Demographics of Elders with MR/SA
Socio-behavioral model
Variable
MR/SA
(N=350)
NoMR/SA
(N=48,014)
Test
Predisposing Characteristics
Gender (male)
238 (68%)
25,064 (52%)
OR=1.9***
Mean age (SD)
70 (5)
73 (7)
t=4.5*
Race (white)
260 (74%)
28,741 (59%)
(SSI/SSDI)
199 (57%)
29,131 (61%) NS
Enabling resources
Dually eligible
303 (87%)
43,226 (90%)
OR=0.7*
FFS coverage
262 (75%)
34,283 (71%)
Low state SA coverage
141 (40%)
16,899 (35%)
OR= OR= 1.2*
Urban location
213 (61%)
30,027 (63%)
Need factors
SMI diagnosis
151 (43%)
7,540 (16%)
OR= 4.1***
Long-term SA diagnosis
19 (5%)
5,549 (12%)
OR= 0.4***
***p<.001 *p<.05

26. Bimodal Example Diagnostic Category Anxiety disorders Eating disorders
Mood disorders
Personality disorders
Psychotic disorders
Number of Clients
(Frequency) 8 4 3 5

27. Measures of Dispersion

28. Dispersion
Definition- Measures the dispersion of responses of a given variable. i.e. Age
Types
Range
Standard deviation

29. The Range Easiest measure to calculate and simplest to understand
Weakest measure
Influenced by outliers (extreme measures) in your data set
Example- Age range of adolescent girls in outpatient treatment for Oxycontin at Victory Programs. Range= years of age.

30. Standard Deviation (SD)
Most commonly used measure of dispersion around a mean
how “spread out” are the values.
Tells us how far the average scores varies from the mean
Measured at the interval or ratio level and sometimes the ordinal level
The smaller the SD, the more the scores cluster around the mean.
The larger the SD, the more the scores are spread or dispersed away from the mean. Never a negative value.
Goal+ small SD as it will be a more representative of the mean (average) of the data

31. Demographics Table

32. Text Supporting Previous Table

33. Buzi et al. (2007) Reading What is the study rational?
What is the mean age of the participants? Range?
What scale was used to measure depression? What is the reliability of this scale?
What is the frequency of individuals who reported having at least one drink of alcohol in the past 30 days?

34. Narrative Example

35. Qualitative Example- Demographics

36. Choosing Measures of Central Tendency- Variable Types
Measures Mean Median Mode
Best Uses
Interval or ratio data
Ordinal, interval, or ratio data
Nominal, ordinal, interval, or ratio data