1. ScWk 298 Quantitative Review Session

2. Types of Variables Continuous variables:
Categorical variables:
Nominal: categories with no ranking (e.g. gender, race/ethnicity, place of birth, etc.)
Ordinal: categories with a ranking (e.g. educational level, income categories, Likert scales (strongly agree, agree, disagree, strongly disagree) etc.)
Continuous variables:
A zero point and equal distance between values (e.g. age, height, weight, # of hours studying a day, etc.).

3. What kind of variable is it?
The number of visits to a homeless shelter ________
How satisfied are you on a scale of 1 (Very satisfied) to 5 (Very dissatisfied)? _______
Have you ever had training in Cognitive Behavioral Therapy? (Yes/No)? ____
Student’s GPA _____

4. Descriptive Statistics: Categorical Variables
Frequencies and percentages are used with categorical variables
Frequency: a count
Percentage: a proportion of the total

5. Which one should you report?
Child.Gender
Frequency
Percent
Valid Percent
Cumulative Percent
Valid F
1955
48.8
50.6 M
1909
47.6
49.4
100.0
Total
3864
96.4
Missing X
146
3.6
4010
What’s the difference between Percent, Valid Percent and Cumulative Percent?
Which one should you report?

6. Descriptive Statistics: Continuous Variables
Means, medians, standard deviations, and ranges are used with continuous variables
Mean: average (add all values and divide by total number of values)
Median: when all of the values are put in order from lowest to highest, the median is the middle number
*Use median instead of mean when there are outliers (extreme high or low values)

7. Descriptive Statistics: Continuous Variables
Standard deviation: A number that reflects how much variation there is from the average value in the dataset. A large SD indicates a lot of values that are different from the mean. A small SD means there are not a lot of values that are different from the mean.
Range: The highest value in the dataset minus the smallest value in the dataset

8. Descriptive StatisticsN
Minimum
Maximum
Mean
Std. Deviation
child age in months
4010
.00
215.00
Valid N (listwise)

9. Testing Hypotheses
Bivariate statistics test the strength of the relationship between TWO variables.
Chi-Square test examines the relationship between a categorical independent variable and a categorical dependent variable

10. Chi-Square Example Research Scenario
Quasi-experimental group research design examining employment outcomes among participants in a vocational training program compared to those on a waiting list (comparison group).
Independent Variable: Vocational Training Program vs. waiting list
Dependent Variable: Employment outcomes

11. Chi-Square SPSS Results
This is APA format for reporting Chi-Square results:
X2 = (2, N = 59) = , p = .003

12. Dependent Samples T Test
Used with a categorical independent variable and a continuous dependent variable
Compares Pre-test data to Post-test data

13. Dependent Samples T-test: Pre-test and Post-tests
Research Scenario
Quasi-experimental pre-test post-test design examining changes in client work skills after participation in a vocational training program
Independent Variable is the vocational training program (pre-test vs. post-test)
Dependent Variable is a score on an assessment of work skills

14. Dependent T-test SPSS Results
This is APA format for
reporting Dependent
T-test results:
t(39) = , p < .001

15. Independent Samples T Test
Used with a categorical independent variable and a continuous dependent variable
Compares data between 2 different groups

16. Independent Samples T-test
Research Scenario
Quasi-experimental group research design examining differences in the number of emergency psychiatric hospitalizations experienced by mental health clients who are either in 1) individualized medication support program versus, 2) a group medication support program.
Independent Variable is the type of medication support program (individualized vs. group)
Dependent variable is the number of emergency psychiatric hospitalizations

17. Independent Samples T-test SPSS Results
This is APA format
for reporting
Independent T-test Results
t(36) = 2.324, p = .026

18. Correlation
Used with a continuous independent variable and a continuous dependent variable
Tests the strength of the association between the 2 variables

19. Correlation Research Scenario
A cross-sectional survey study is examining the possible association between employee stress levels and the number of clients they have on their caseload.
Independent variable is number of clients
Dependent variable is score on a stress survey

20. Correlation SPSS Results
This is APA format for reporting correlation results
r (40) = .821, p < .001

21. Other tests
ANOVA: One categorical independent variable (with 3 or more categories) and one continuous dependent variable
Multivariate statistics allow you to determine the impact of an independent variable on a dependent variable while factoring out the influence of potentially confounding (i.e. extraneous) variables. Logistic regression is for a categorical dependent variable and multiple linear regression is for a continuous dependent variable

22. SPSS Resources
Research Sequence website has self-guided SPSS labs with answer keys:
UCLA SPSS website (LOTS of examples with detailed instructions):