# SW 594 B

 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 DV - Dependent variable - Outcome / Effect - The value depends of the cause (independent variable) - The variable that is measured 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 = Other1 = male, 2 = female. Ordinal - Categorical - Mutually Exclusive - Ranked in order Examples: RANK: 1st place, 2nd place,. last place LEVEL OF AGREEMENT: No, Maybe, Yes 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 Ratio - Continuous - Scale - Absolute zero - Scale responses: "HAVE" true meaning between scores Examples: INCOME: money earned last year NUMBER of children GPA: grade point average 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 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 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 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 Measures of Central Tendency - Univariate Analyses- Descriptive Statistics (describes something about your populations) - Mode = Most frequent score - Median = Mid point (n+1/2), # line- Mean = Averge (add scores togther divide by x) Measures of Dispersion/Variability - Univariate Analyses - Descriptive Statistics (describes something about your populations) - 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)= Null Hyothesis -Ho - If significance is found reject the Null (Ho) - Ho = pre means + post means Alternative Hyothesis -Ha- If significance is found you fail to reject the Alternative (Ha) (accept Ha) - Ha = pre means ≠ means 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 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 Cross Tabulation - Bivariate Analyses (2 variables)- Includes 2 catergorical variables - Table to display cell count (2x2; 3x2)(define rows (R) and column (C)) Chi Square x 2 - Bivariate Analyses (2 variables) - Includes 2 catergorical variables - Test of ASSOCIATION (difference) between 2 catergorical variables - Phi (Correlation) 2 + variables Quantatative Research - Univariate Analysis - Bivariate Analysis - Datad ananylses, results, findings, conclusions - The use of surveys, scales, instruments, charts 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 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 Hypothesis example Chi Square X2: Ho : Odds = 1Ha : Odds ≠ 1 ANOVA: AssociationExample: Depression vs girls vs boys vs transgender Ho: Xboys = Xgirls = Xtransgender Ha: Xboys ≠ Xgirls ≠ Xtransgender Independent t -test--> Ho: Xgirls = XboysHa: X girls≠ Xboys Dependent t -test-->Ho: Xpre mean = Xpost meanHa: Xpre mean ≠ Xpost mean Authorreyesveronica09 ID73688 Card SetSW 594 B DescriptionMid Term Study Guide, March 2011 Updated2011-03-20T21:44:46Z Show Answers