# SW 594 B

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 Author: reyesveronica09 ID: 73688 Filename: SW 594 B Updated: 2011-03-20 17:44:46 Tags: Research Study Guide Folders: Description: Mid Term Study Guide, March 2011 Show Answers:

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1. IV
• - Independent variables
• - Influence (influences another)
• - Cause / Predictor
• - Can be manipulated
• - Its's value does not depend on another variable - The value that is manipulated - For example: - Does "GENDER" have an influnce on depression? "Gender"= IV
2. DV
• - Dependent variable
• - Outcome / Effect

• - The value depends of the cause (independent variable)
• - The variable that is measured
3. Nominal
• - Categorical
• - Mutually Exclusive
• - Not ranked in order

• Examples:
• MEAL PREFERENCE: Breakfast, Lunch, Dinner
• RELIGIOUS PREFERENCE: 1 = Buddhist, 2 = Muslim, 3 = Christian, 4 = Jewish, 5 = Other
• 1 = male, 2 = female.
4. Ordinal
• - Categorical
• - Mutually Exclusive
• - Ranked in order

• Examples:
• RANK: 1st place, 2nd place,. last place
• LEVEL OF AGREEMENT: No, Maybe, Yes
5. Interval
• - Continuous
• - Scale
• - No absolute zero
• - Scale responses- NO true meaning between scores

• Examples: TIME OF DAY on a 12-hour clock -
• What is your bed time? 8-9; 9-10; 10-11; 11-12
6. Ratio
• - Continuous
• - Scale
• - Absolute zero
• - Scale responses: "HAVE" true meaning between scores

• Examples: INCOME: money earned last year
• NUMBER of children
7. Example Question:
Level of measurement
- Are you serious about attending a post crisis workshop? Yes/No

- Do you believe in the death penalty? Yes/No

- What is your favorite color? Blue/ Green/Red

Nominal
8. Example Question: Level of measurement
- How serious are you about attending a post crisis workshop? Extremely serious/very serious/somewhat serious/ not serious

- How satisfied are you with the MSW program at CSULB? Very satisfied, Somewhat satisfied, Not satisfied

Ordinal
9. Example Question: Level of measurement
On a scale from 1-10, respond to how serious you are about attending a post crisis workshop? 1=not at all; 10=extremely serious

- The death penalty is justified in certain cases? 5:Strongly agree; 4:Agree; 3: Neutral; 2:Disagree; 1: Strongly disgree

Interval
10. Example Question: Level of measurement
- How many clients have you had in the last six months?

- How many days would you attend a 20 day post crisis workshop if offered?

Ratio
11. Measures of Central Tendency
• - Univariate Analyses

• - Mode = Most frequent score
• - Median = Mid point (n+1/2), # line
• - Mean = Averge (add scores togther divide by x)
12. Measures of Dispersion/Variability
• - Univariate Analyses

• - Range + Interquarrite Range (mid 50%) difference between the largest and smallest values (Take the largest score and subtracts from the smallest score)
• - Standard Diviation = Square root of s2 = +/- SD
• - Variance (s2)=
13. Null Hyothesis
• -Ho
• - If significance is found reject the Null (Ho)

- Ho = pre means + post means
14. Alternative Hyothesis
• -Ha-
• If significance is found you fail to reject the Alternative (Ha) (accept Ha)

- Ha = pre means ≠ means
15. Correlation (r) Relationships
- Ho --> r = 0

- Ha --> r ≠ 0

• -"p" value -
• -> Less than 0.05=Significance
• -> Greater than 0.05- NO Significance

- Phi, pb (Point Biserial), Cramer's V
16. P Value
• - Significance
• - How much possible evidence do we have aginst the null

"p" value:

• -> Less than 0.05 or equal to =Significance
• -> Greater than 0.05 or equal to- NO Significance
17. Cross Tabulation
• - Bivariate Analyses (2 variables)
• - Includes 2 catergorical variables

- Table to display cell count (2x2; 3x2)(define rows (R) and column (C))
18. Chi Square x 2
• - Bivariate Analyses (2 variables)
• - Includes 2 catergorical variables

- Test of ASSOCIATION (difference) between 2 catergorical variables

- Phi (Correlation) 2 + variables
19. Quantatative Research
- Univariate Analysis

- Bivariate Analysis

- Datad ananylses, results, findings, conclusions

- The use of surveys, scales, instruments, charts
20. t -test
- Bivariate Analyses

• - Catergorical IV (2 independent grps)
• - Categorical DV (mean scores (differnce))

- Test of ASSOCIATION (difference)

Ho --> Mean score (boys) = Mean score (girls)

• Independent t -test-->
• - X (pre mean) ≠ X (post mean) (independent of each other)

• Dependent t -test--> PAIRED
• - X (pre mean) WITH X (post mean) (paired together)

• ANOVA--> one IV (3 means)
• - X boys (pre mean) ≠ X girls ≠ Xtrans
21. Relationships
(same as Independent t test except looking at realtionhsip)
• - pb - Point Biseral Correlation
• -->1 catergorical IV; 1 continuous DV

• -ETA (NOT ON TEST)
• -->2 catergoriacl IV's; 1 continuous DV
22. Hypothesis example
Chi Square X2:

• Ho : Odds = 1
• Ha : Odds ≠ 1

• ANOVA: Association
• Example: Depression vs girls vs boys vs transgender

Ho: Xboys = Xgirls = Xtransgender

Ha: Xboys ≠ Xgirls ≠ Xtransgender

• Independent t -test-->
• Ho: Xgirls = Xboys
• Ha: X girls≠ Xboys

• Dependent t -test-->
• Ho: Xpre mean = Xpost mean
• Ha: Xpre mean ≠ Xpost mean

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