SW 594 B

  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
    • GPA: grade point average
  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
    • - 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)
  12. 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)=
    • Image Upload 2
  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
Author
reyesveronica09
ID
73688
Card Set
SW 594 B
Description
Mid Term Study Guide, March 2011
Updated