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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 = Other
 1 = 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 12hour clock 
 What is your bed time? 89; 910; 1011; 1112

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 110, 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 s^{2 = }+/ SD
  Variance (s^{2})=

Null Hyothesis
 H_{o }
  If significance is found reject the Null (H_{o})
 H _{o} = pre means + post means

Alternative Hyothesis
 H_{a}
 If significance is found you fail to reject the Alternative (H_{a}) (accept H_{a})
 H _{a} = pre means ≠ means

Correlation (r) Relationships
 H_{o }> r = 0
 H_{a} > 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)
H _{o} > 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 }≠ X_{trans}

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 X^{2}:
 H_{o} : Odds = 1
 H_{a} : Odds ≠ 1
 ANOVA: Association
 Example: Depression vs girls vs boys vs transgender
H _{o}: X _{boys }= X _{girls} = X _{transgender
}H _{a: }X _{boys ≠ }X _{girls ≠ }X _{transgender
Independent t test> Ho: Xgirls = XboysHa: X girls≠ Xboys
Dependent t test>Ho: Xpre mean = Xpost meanHa: Xpre mean ≠ Xpost mean
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