Post on 10-Nov-2015
description
transcript
.
.
Variable comes from Vary + Able So variable is anything that its values can be varied. Variables are the phenomena or the topics that
researchers want to investigate.
Variable
.
Variable
Describe
?
?
?
.
VariableCorrelational Relationship
Variable
? ? ?
?
?
.
Difference Testing or Measure of Association
Descriptive Statistics or Inferential Statistics
Univariate, Bivariate, Multivariate
.
?
.
, ..
(Attribute)
(Object)
(Number)
(Measure)
.
(Attribute)
(Object)
(Number)
(Measure)
,
(Test, Measure) (Construct Variable) (Subject) (Variable Values)
.
TOEFLS
JDI
(Job Description Index)
(Job Satisfaction)
.
Nominal Scale Ordinal Scale Interval Scale Ratio Scale
.
Nominal , = 1, = 2Ordinal ,
= 1 = 2 = 3
Interval , 0
0
1 = 2.84 2 = 3.65 3 = 4.12
Ratio , 0
0
1 = 0 2 = 7,650 3 = 9,000
.
(Categorical or Discrete Variable) Nominal Scale Ordinal Scale
(Numerical or Continuous Variable) Interval Scale Ratio
.
(Descriptive Statistics) (Inferential Statistics)
.
2
.
(Descriptive Statistics)
Population Parameter , ,m s r
.
(Descriptive Statistics) (Inferential Statistics)
Population
Sample
Parameter
Statistic Mean, SD., r.
, ,m s r
Hypothesis Testing
.
Parameter Statistic
Parameter and Statistic
.
(Central Tendency) (Variation) (Distribution)
.
CentralTendency
VariationDistribution
.
.
Ordinal Scale Interquartile Range
Interval and Ratio Scale Range (Max. - Min.) Variance Standard Deviation
.
(Skewness) (Kurtosis)
.
(Normal Distribution)
MeanMedian
Mode
Symmetric Bell Shaped Mean = Median = Mode
.
(Negatively Skewed)
Mode
Median
Mean
.
(Positively Skewed)
Mode
Median
Mean
.
(Kurtosis)
Platykurtic Leptokurtic Mesokurtic
.
What is random variable?
(Random Variable)
Random Variable = Random Sample + Variable
.
(Probability) 100 20 80
1 100 ?
1
Probability
.
= 20/100 = .20 = 80/100 = .80 1
Probability
.
2 1 20 80 2 80 20
10 3 7 ?
2
Probability
.
10 2
Probability
.
0 1 2
1 1 2
::
HH
m mm mm m
=
1 20 80
2 80 20
7 3 (p=.03)
H1 2
= 10
(p=.03)
.
E K 4 7
.
H0 : = H1 :
E K 4 7
.
Verification Karl Poppers Falsification
Logic of Disconfirming Null Hypothesis Null hypothesis can never be proved to be true, only
falsifying or disconfirming
.
Type I and Type II Error
Type I Error H0 H0 Type II Error H0 H0
.
Type I and Type II Error
Type I Error False Alarm (Alpha)
Type II Error Failed Alarm (Beta)
.
Type I and Type II Error
Effect Size
.
Null Hypothesis
(Effect Size)
Power
.
Type I and Type II Error
H0 : H1 : Type I Error =
Type II Error =
.
Type I and Type II Error
H0 : A 1 .. H1 : A 1 .. Type I Error = A
1 .. Type II Error = A
1 ..
.
Type I and Type II Error
H0 : HIV H1 : HIV Type I Error =
HIV
Type II Error = HIV
.
Type I and Type II Error
H0 : H1 : Type I Error =
Type II Error =
.
Type I and Type II Error
Type I Type II Error
Type I Type II Type II
.
Significance Testing
Confidence Interval
.
Point Estimate Interval Estimate
Confidence Interval
Confidence Interval = Statistics + Sampling Error (Margin Error)
Confidence Interval
X Zn
s
.
(Precision)
H0
Confidence Interval
.
Confidence Interval
X Zn
s
200010,200 1.96400
10,200 196 10,004 10,396 = -
.
Statistical Significance Practical Significance
Significance
.
Types of Statistics
Difference Testing Statistics (Descriptive Statistics) t-test, ANOVA, ANCOVA, Chi-Square
Measures of Association Statistics Pearsons r, Regression, Canonical Correlation
Interdependence Statistics Factor Analysis, Cluster Analysis, Multidimensional
Scaling
.
Types of Statistics
Parametric Statistics t-test, ANOVA, Pearsons r, Regression
Nonparametric Statistics Mann-Whitney U, Wilcoxon, Kruskal Wallis
Robust Statistics Trimmed Mean, Winsorized Mean, Winsorized Variance,
Resampling Statistics
.
(Bivariate Statistics)
t-test Y = X Y X
2 Independent Sample
Assumptions Mann-Whitney U Dependent Sample
Assumptions Wilcoxon Signed Rank
.
(Bivariate Statistics)
ANOVA Y = X Y X
2 ANOVA
Assumptions Kruskal Wallis ANOVA for Repeated Measures
Assumptions Friedman Test
.
(Bivariate Statistics)
Pearsons r Y = X Y X
Chi-square Y = X Y X
.
(Multivariate Statistics)
Multiple Regression Manova (Multivariate Analysis of Variance) Discriminant Analysis Canonical Correlation Factor Analysis
.
(Multivariate Statistics)
Multiple Regression Y = X1+X2+X3+...+Xn Y X
( Dummy) Manova (Multivariate Analysis of Variance)
Y1+Y2+Y3+...Yn = X1+X2+X3+...+Xn Y X
.
(Multivariate Statistics)
Discriminant Analysis Y = X1+X2+X3+...+Xn Y X
( Dummy) Canonical Correlation
Y1+Y2+Y3+...Yn = X1+X2+X3+...+Xn Y X
.
(Multivariate Statistics)
Factor Analysis X1+X2+X3+...+Xn X
Multilevel Analysis Y = X1+X2+X3+...+Xn Y X
( Dummy) X
.
Multilevel Analysis
(OCB)
1. (JS) 2. (ORCOM) 3.
(POS) 4. (MOTIV)
1. (LEADER)
2. (CULTURE)
.
Structural Equation Modeling
EDU
DURA
EXP
TF
CH INS INT IND
QUAL
Q4
Q3
Q2
Q1
Q5
Q6
.