Upload
heaven-veasey
View
213
Download
0
Tags:
Embed Size (px)
Citation preview
KNOWLEDGE INQUIRY
How we know, what we know
and
How we know,
we know
Bouma Gary D. & G.B.J.Atkinson. (1995) A Handbook of Social Science Research. p.3
Description
Explanation
Prediction
Control
Development
Exploration
What, When Where, How
Why
PURPOSE OF RESEARCH
Confirmatory Factor Analysis & Path Analysis
Interest Idea Theory
? YY ?
X YA B
?
? A B C D E
F G H I
Conceptualization
Specify the meaning of the concepts and
variables to be studied.
Operationalization
How will we actually measure the variables
under study?
Choice of Research MethodExperimental Research
Survey Research Field Research Content Analysis Existing Data Research Comparative Research Evaluation Research Mixed Design
Population & SamplingWhom do we want to be
able to draw conclusions about?
Who will be observed for the purpose?
Observation
Collecting data for analysis and
interpretation
Data Processing
Transforming the data collected into a form
appropriate to manipulation and analysis
Analysis
Analyzing data and drawing conclusions
Application
Reporting results and
assessing their implications.
1
2
3
5
4
6
7 8 9
RESEARCH PROCESS & DESIGN
Interest Idea Theory
? YY ?
X YA B
?
? A B C D E
F G H I
Conceptualization
Specify the meaning of the concepts and
variables to be studied.
Operationalization
How will we actually measure the variables
under study?
Choice of Research MethodExperimental Research
Survey Research Field Research Content Analysis Existing Data Research Comparative Research Evaluation Research Mixed Design
Population & SamplingWhom do we want to be
able to draw conclusions about?
Who will be observed for the purpose?
Observation
Collecting data for analysis and
interpretation
Data Processing
Transforming the data collected into a form
appropriate to manipulation and analysis
Analysis
Analyzing data and drawing conclusions
Application
Reporting results and
assessing their implications.
1
2
5
7 9
RESEARCH PROCESS & DESIGN
AnalysisDesign
MeasurementDesign
SamplingDesignResearch
Design
Data CollectingDesign
3
6
4
8
Interest Idea Theory
? YY ?
X YA B
?
? A B C D E
F G H I
Conceptualization
Specify the meaning of the concepts and
variables to be studied.
Operationalization
How will we actually measure the variables
under study?
Choice of Research MethodExperimental Research
Survey Research Field Research Content Analysis Existing Data Research Comparative Research Evaluation Research Mixed Design
Population & SamplingWhom do we want to be
able to draw conclusions about?
Who will be observed for the purpose?
Observation
Collecting data for analysis and
interpretation
Data Processing
Transforming the data collected into a form
appropriate to manipulation and analysis
Analysis
Analyzing data and drawing conclusions
Application
Reporting results and
assessing their implications.
RESEARCH PROCESS & DESIGN
Cross-sectional Study254721-3031-4041-50
One-point of time
Trend Study2547 255721-30 21-3031-40 31-4041-50 41-50
Same framework & instruments
Cohort Study2547 255721-30 21-3031-40 31-4041-50 41-50
51-60
Same framework & instruments
Panel Study2547 255721-30 21-3031-40 31-4041-50 41-50
51-60
Same individuals
RESEARCH DESIGN: TIME DIMENSIONS
Validity = Accuracy = Low BiasReliability = Precision = Low Variance
Pro
babili
ty
Densi
ty
Precision
Reference value
Accuracy
Value
Parameter
Statistics
Validity and Reliability of Research Finding
Low Validity = Low Accuracy = High BiasLow Reliability = Low Precision = High Variance
Pro
babili
ty
Densi
ty
Low Precision
Reference value
Low Accuracy
ValueParameter
Statistics
Low Validity and Low Reliability
Low Validity = Low Accuracy = High BiasHigh Reliability = High Precision = Low Variance
Pro
babili
ty
Densi
ty
Precision
Reference value
Low Accuracy
ValueParameter
Statistics
Low Validity and High Reliability
A test with low validity because of
low reliability
A highly valid test A reliable test with low validity.
Validity and Reliability of Measurement
Null Hypothesis Testing
Goal: To determine if the independent variable has a statistically significant (real) effect on the dependent variable. That means, an effect that is UNLIKELY to be due to chance variations or sampling error.
The null hypothesis
• Researchers make the initial assumption that the independent variable manipulation will have NO EFFECT on the dependent variable (will be null).
• Under the “null hypothesis”, any observed difference between groups is assumed to be due to chance (random error) unless proven otherwise!
• Inferential statistics are the tools used to resolve this question.
Inferential Statistics
• Tools for testing how likely it is that the results of a study are due to error or chance variation.
• It is always possible that differences between groups & Relationship at the end of the study may have been due to sampling error, rather than being due to the independent variable.
• Sampling error: the extent to which the groups were different at the start of the study.
Inferential Statistics
• Statistical Significant: • Type I error ()• Type II error ()• Power of test (1-)• Confidence Interval (1-)
• Practical Significant: • Effect size (2, 2)• Sample Determination
DESCRIPTIVE STATISTICS
Mean
Standard deviation
Variance, Covariance
Frequency & Percentage & ratio
Percentile, quartile
Median & mode
Range, etc.
TESTING FOR ASSUMPTION OF STATISTICS
Kurtosis
Skewness
Normal Distribution
Multivariate Normality
Multicolinearity
Linearity
Outliers
Mean (Y)
Mean (X1
)
Mean (X2
)
Mean (X3
)
Descriptive Statistics: How Importance?Measure of Central Tendency: Mean, Mode, MedianMeasure of Dispersion: Variance, Standard Deviation, Mean Deviation, Range
Univariate: Variable, Variation & Variance
2X1 2
X2 2X3 2
Y
YX1 X2 X3
Descriptive Statistics:Mean Vector
variance-covariance matrix
Bivariate: Variables, Variance & Covariance
2X1
2X2 2
X3 2Y
2X1 2
X2 2X3 2
Y
Bivariate: Variables, Variance & Covariance
Cov (X1,Y)
Cov (X1,X2
)
Cov (X1,X3
) Cov (X2,X3
)
Cov (X2,Y)
Cov (X3,Y)
Cov (X1,Y)
Cov (X1,X2
)
Cov (X1,X3
) Cov (X2,X3
)
Cov (X2,Y)
Cov (X3,Y)
10
Y
1 00 10 0
1 0 00 1 00 0 10 0 0
d1 d2 d1 d2 d3
ตั�วแปรสั�งเกตัได้ Observed variable(Nominal Scale)Observed
variable(Interval Scale)1 Latent
variable
Causal relationship
Relationship
d1
1
สั�ญลั�กษณ์�แลัะความหมายที่��ใช้�
10
X1
Y
One-way ANOVA (Independent sample t-test)
Statistical Design
Ypo
st
Ypre
One-way ANOVA with repeated measured (Dependent sample t-test)
Within-subjects Design
?
?
Different
DifferentChange, Gain, Development
Between-subjects DesignDirect
effects
Direct effects
Bivariate Correlation Analysis (rxy)
YX
rx
yYX Z
Cov(x,y)
rx
y
ry
z
rx
z
Cov(x,z)
Cov(y,z)
Cov(x,y)
Standardized Score
Raw Score
Statistical Design
X1
X2 X3
Y?
Statistical DesignPartial & Part Correlation Analysis
(Spurious or Indirect Causality)Direct effects
X1
Y
1 0
0 1
0 0
One-way ANOVA (F-test)
YT2YT1
One-way ANOVA with repeated measured
Within-subjects Design
YT2
?
? ?
?
Between-subjects Design
Statistical Design
Direct effects
Direct effects
10X
1 Y
Two-way ANOVA (additive model) -- >No interaction effects
X2
1 0
0 1
0 0
Main effect-X1
Main effect-X2
Between-subjects Design
Statistical Design
Direct effects
10X
1 Y
Two-way ANOVA (non-additive model) -- > Interaction effects
X2
1 0
0 1
0 0
Main effect
Main effect
Interaction effect
Between-subjects Design
Direct effects
Statistical Design
Y
10
10
1 00 10 0
Multi-way ANOVA (the interactive structure)
X1
X2
X3
Between-subjects Design
Statistical Design
Direct effects
Interaction effect
Interaction effect
Main effect
Main effect
Main effect
Y
One-way Analysis of Covariance (ANCOVA) additive model
X1
1 0
0 1
0 0
(Covariate)
Z
? Between-subjects Design
Statistical Design
Y10
1 00 10 0
Two-way ANCOVA (Interactive structure)
Z
X1
X2
(Covariate)
Between-subjects Design
Statistical Design
Direct effects
Main effect
Interaction effect
Main effect
Interaction effect Main
effect
X1
X2
X3
Y
Simple Regression Analysis (SRA)Multiple Regression Analysis (MRA) (Convergent Causal structure)
No Correlatio
n(r = 0)
Direct effects
y.x1
y.x2
y.x3
X Yy.x
YX
rx
y
Statistical Design
X1
X2
X3
Multivariate Multiple Regression Analysis (MMR)(Convergent Causal structure two or several times)
Y1
Y2
Direct effects
No Correlatio
n(r = 0)
Statistical Design
10
X1
X2
X3
Two-groups Discriminant Analysis (Discriminant structure)Binary Logistic Regression Analysis
(Y)
W
W
W
Direct effects
No Correlatio
n(r = 0)
Statistical Design
X1
X2
X3
Multiple Discriminant Analysis(Discriminant Structure with more than two population groups)
1 0
0 1
0 0
(Y)
W
W
W
Direct effects
No Correlatio
n(r = 0)
Statistical Design
Y1
10
10
1 00 10 0
Multivariate Analysis of Variance -- MANOVA(Interactive Structure two or several times)
Y2
X1
X2
X3
Statistical Design
Main effectInteraction
effect
Interaction effect
Main effect
Main effect
Y1
10
1 00 10 0
ZY2
Multivariate Analysis of Covariance -- MANCOVA (Interactive Structure two or several times)X
1
X2
(Covariate)
Statistical Design
Main effectInteraction
effect
Interaction effect
Main effect
Main effect
U1 V1
Canonical variates
(Independent)
Canonical variates
(Dependent)
U2 V2
RC1, 1
X1
X2
X3
X4
Y1
Y2
Set of Independe
nt variables
Set of Dependent variables
Canonical Function-1
RC2, 2
Canonical Loading2
Canonical Loading2
Simple Correlatio
n
Simple Correlatio
n
Canonical Correlation Analysis (CCA)
Canonical weight
Canonical Weight
Canonical Function-2
Statistical Model
(Conceptualization)High
Low(Operationaliza
tion)
Level of
Ab
str
acti
on
Concept &
Construct
Variables
Indicator Indicator Indicator
Item Item Item Item Item Item Item Item Item
Conceptual Definition
Theoretical Definition
Real Definition
Operational Definition(How to
measured?)
Generalized idea
Communication
Real worldHypothesis
testing
Time, Space, Context
Test-1 Test-2 Test-n
Principle Component Analysis (PCA)
2 31
X1 X2 X3 X4 X5 X6 X7 X8 X9
The Component Loading or the Structure/Pattern Coefficient
Factor structure / Component / Dimensions / Unmeasured variables
Measured variables (Observed) / Indicators / Items
Statistical Design
Measured variables
(Observed) / Indicators / Items
2 31
X1 X2 X3 X4 X5 X6 X7 X8 X9
The Factor Loading or the Structure/Pattern Coefficient
Factor structure /
Component / Dimensions / Unmeasured
variables
Exploratory Factor Analysis (EFA) with Orthogonal Rotation
Errors or Uniqueness
Statistical Model
Measured variables
(Observed) / Indicators / Items
2 31
X1 X2 X3 X4 X5 X6 X7 X8 X9
The Factor Loading or the Structure/Pattern Coefficient
Factor structure /
Component / Dimensions / Unmeasured
variables
Exploratory Factor Analysis (EFA) with Oblique Rotation
Errors or Uniqueness
Statistical Model
2,1
3,1
3,2
Measurement Model:Construct X with 3 subdimensions or 3 factors
2 31
X1 X2 X3 X4 X5 X6 X7 X8 X9
2,1
3,1
3,2
2,11,1 3,1 4,2 5,2 6,2 7,3 8,3 9,3
Statistical Model
Measured variables
(Observed) / Indicators /
Items
2 31
X1 X2 X3 X4 X5 X6 X7 X8 X9
The Factor Loading or the Structure/Pattern Coefficient
Latent Construct
Unmeasured variables
Errors or Uniqueness
Confirmatory Factor Analysis (CFA)
2,1
3,1
3,2
Some Errors are correlated
Some Factors are correlated/ Some Factors are not correlated
2,11,1 3,1 4,2 5,2 6,2 7,3 8,3 9,3
Statistical Design
12345678
91011121314151617
18192021222324252627
282930313233
x1x2x3x4x5x6x7x8
x9x10x11x12x13x14x15x16x17
x18x19x20x21x22x23x24x25x26x27
x28x29x30x31x32x33
F-1
F-2
F-3
F-4
First-order Confirmatory Factor Analytic Model
2,1
3,2
4,3
3,1
4,2
4,1
Sta
tist
ical D
esi
gn
: Fi
rst-
ord
er
Fact
or
An
aly
sis
12345678
91011121314151617
18192021222324252627
282930313233
x1x2x3x4x5x6x7x8
x9x10x11x12x13x14x15x16x17
x18x19x20x21x22x23x24x25x26x27
x28x29x30x31x32x33
F-1
F-2
F-3
F-4
F-A
F-B
Second-order Confirmatory Factor Analytic Model
Sta
tist
ical D
esi
gn
: Seco
nd-o
rder
Fact
or
An
aly
sis
M-1
x1x2x3x4x5x6x7x8
x9x10x11x12x13x14x15x16x17
x18x19x20x21x22x23x24x25x26x27
x28x29x30x31x32x33
LV-1
LV-2
LV-3
LV-4
M-2
Sta
tist
ical D
esi
gn
: M
ult
itra
its-
Mult
imeth
ods
Matr
ix
Analysis UsingDependent & Interdependent
Techniques
Sakesan Tongkhambanchong, Ph.D (Applied Behavioral Science Research)
Y
X1
X2
X3
Causal Modeling I: Path Analysis with Observed Variables(Assumption: Measurement error = 0)
Y
X1
X2
X5X4
Total Effect = Direct + Indirect Effects
Total Effect = Direct + Indirect Effects
X3
Statistical Design
2
1,1
2,1
3,1
2Y6,
2
Y4,
2Y5,
2
1X3,
1
X1,
1X2,
1
2X6,
2
X4,
2X5,
2
1Y3,
1
Y1,
1Y2,
1
Causal Modeling II: Path Analysis with Latent Variables Linear Structural Equation Modeling (SEM)(Assumption: Measurement error > 0)
4,2
1,1
5,2
6,3
2,1
3,1
4,2
5,2
6,2
1
Total Effect = Direct + Indirect Effects
Path Analysis + Confirmatory Factor Analysis
Statistical Design