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Sakesan Tongkhambanchong, Ph.D.(Applied Behavioral Science Research) Faculty of Education, Burapha University.

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Presentation on theme: "Sakesan Tongkhambanchong, Ph.D.(Applied Behavioral Science Research) Faculty of Education, Burapha University."— Presentation transcript:

1 Sakesan Tongkhambanchong, Ph.D.(Applied Behavioral Science Research) Faculty of Education, Burapha University

2 Introduction to SEM –Research Process & Designs –Statistical Designs & Models –Variance & Covariance Matrix (CM) & Correlation Matrix (KM) –LISREL’s Matrix –MRA: Multiple Regression Analysis by LISREL –MMRA: Multivariate Multiple Regression Analysis by LISREL Confirmatory Factor Analysis (CFA) –First-order CFA –Second-order CFA Structural Equation Modeling (SEM)

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6 Interest Idea Theory ? Y Y ? X Y A 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 Method Experimental Research Survey Research Field Research Content Analysis Existing Data Research Comparative Research Evaluation Research Mixed Design Population & Sampling Whom 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

7 Interest Idea Theory ? Y Y ? X Y A 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 Method Experimental Research Survey Research Field Research Content Analysis Existing Data Research Comparative Research Evaluation Research Mixed Design Population & Sampling Whom 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

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9 Probability Density High Variance Low Precision Reference value High Bias Low Accuracy Value Parameter Statistics

10 Probability Density High Precision Low Variance Reference value High Bias Low Accuracy Value Parameter Statistics

11 Probability Density Precision Reference value Accuracy Value Parameter Statistics

12 AB SN PBC Intention Behavior Research Conceptual Framework Hypothesized Model: Causal Model (if X then Y) Statistical Design: Structural Equation Model (SEM) Time-1Time-2Time-3 Nature of Model: Longitudinal Design (3-points of time)

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14 Emotional Capital psychological well-being psychological well-being Affect Balance Resilience Ultimate Dependent Variable Mediator Variable Exogenous Variable Endogenous Variable Independent Variable

15 Emotional Capital psychological well-being Affect Balance Resilience Mindfulness Ultimate Dependent Variable Mediator Variable Exogenous Variable Endogenous Variable Independent Variable

16 Emotional Capital psychological well-being Affect Balance Resilience Mindfulness Ultimate Dependent Variable Mediator Variable Exogenous Variable Endogenous Variable Independent Variable Moderator Variable

17 Emotional Capital psychological well-being Affect Balance Resilience Mindfulness Ultimate Dependent Variable Mediator Variable Exogenous Variable Endogenous Variable Independent Variable

18 Research Conceptual Framework Theory of Planned Behavior :TPB (Ajzen, 1991)

19 Hypothesized Model & Number of Parameter Estimation

20 Testing Hypothesized Model & Parameter Estimated

21 Last Trimming Model & Parameter Estimated

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23 1 0 X X d1d1 d2d2 d1d1 d2d2 d3d3 Observed variable (Nominal Scale) Observed variable (Interval Scale) 11 11 Latent variable Causal relationship Relationsh ip d1d1 11 11 Y Y

24 Mean Mode Median (Y) Mean Mode Median (Y) Mean Mode Median (X1) Mean Mode Median (X1) Mean Mode Median (X2) Mean Mode Median (X2) Mean Mode Median (X3) Mean Mode Median (X3) Descriptive Statistics: How Importance? Central Tendency: Mean, Mode, Median Dispersion: Variance, Standard Deviation, Average Deviation  2 X1  2 X2  2 X3 2Y2Y

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29  2 X1  2 X2  2 X3 2Y2Y Cov (X1,Y) Cov (X1,X2) Cov (X1,X3) Cov (X2,X3) Cov (X2,Y) Cov (X3,Y)

30 Bivariate Correlation Analysis (r xy ) Y Y X X rxyrxyrxyrxy Y Y X X ? Z Z ?? r * xy = (r xy )/sqrt(r xx * r yy ) Measurement error = 0, reliability = 1 r * xy = (0.90)/  (1.0 *1.0 ) = (0.90)/(1.0) = r * xy = (0.90)/  (0.60 *0.70 ) = (0.90)/(0.648) = If r xx or r yy  1.00, Measurement error  0

31 The Misconception: If Pearson’s product–moment correlation, r xy, turns out equal to 0.00, this indicates that there is no relationship between the X and Y scores used to compute that correlation coefficient. Pearson’s r works well only if the relationship between X and Y is linear. If the relationship between the two variables is curvilinear, the value for r will underestimate the strength of the existing relationship

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34 The Misconception: If the data on two variables having similar distributional shapes are correlated using Pearson’s r, the resulting correlation coefficient can land anywhere on a continuum that extends from 0.00 to ±1.00; therefore, an r of +.50 (or –.50) indicates that the measured relationship is half as strong as it possibly could be. Pearson’s r:  1.0 = Perfect correlation  0.8 = Strong correlation  0.5 = Moderate correlation  0.2 = Weak correlation 0.00 = No correlation

35 The coefficient of determination, r 2, is a better measure of relationship strength than the correlation coefficient, r. This is because the square of r indicates the proportion of variability in one of the two variables that is explained by variability in the other variable

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37 The Misconception: A single outlier cannot greatly influence the value of Pearson’s r, especially if N is large. Pearson’s r:

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40 Sakesan Tongkhambanchong, Ph.D (Applied Behavioral Science Research)

41 1 0 X1X1X1X1 Y Y One-way ANOVA (Independent sample t- test) Y post Y pre One-way ANOVA with repeated measured (Dependent sample t-test) One Factor Within- subjects Design ? ? Different Change, Gain, Development One Factor Between- subjects Design Direct effects

42 X1X1X1X1 Y Y One-way ANOVA (F-test) Y T2 Y T1 One-way ANOVA with repeated measured Within-subjects Design Y T2 ? ? ? ? Between- subjects Design Direct effects

43 1 0 X1X1 Y Y Two-way ANOVA (non-additive model) -- > Interaction effects X2X ? Main effect ? Interaction effect ? Between- subjects Design

44 Y Y Multi-way ANOVA (Non-additive model) (the interactive structure) X1X1 X2X2 X3X3 Between- subjects Design

45 Y Y One-way Analysis of Covariance (ANCOVA) additive model X1X (Covari ate) X1 ? Between- subjects Design

46 Bivariate Correlation Analysis (r xy ) Y Y X X rxyrxy Y Y X X Z Z Cov(x, y) rxyrxy ryzryz rxzrxz Cov(x, z) Cov(y, z) Cov(x, y)

47 X1 X2 X3 Simple Regression Analysis (SRA) Multiple Regression Analysis (MRA) (Convergent Causal structure) No Correlation (r = 0) Direct effects  y.x1  y.x2  y.x3 X X  y.x Y Y X X rxyrxy

48 X1 X2 X3 Multivariate Multiple Regression Analysis (MMR) (Convergent Causal structure two or several times) Y1 Y2       Direct effects No Correlation (r = 0)

49 X1 X2 X3 Two-groups Discriminant Analysis (Discriminant structure) Binary Logistic Regression Analysis (Y)(Y)(Y)(Y) W W W Direct effects No Correlation (r = 0)

50 X1 X2 X3 Multiple Discriminant Analysis (Discriminant Structure with more than two population groups) (Y)(Y)(Y)(Y) W W W Direct effects No Correlation (r = 0)

51 Y Multivariate Analysis of Variance -- MANOVA (Interactive Structure two or several times) Y2 X1X1 X2X2 X3X3

52 Sakesan Tongkhambanchong, Ph.D (Applied Behavioral Science Research)

53 Canonical variates (Independe nt) Canonical variates (Dependent ) R C1, 1 Set of Independen t variables Set of Dependent variables Canonical Function-1 R C2, 2 Canonical Loading 2 Simple Correlation Canonical Correlation Analysis (CCA) Canonical weight Canonical Weight Canonical Function-2

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55 Concept & Construct Variables Indicator Item Conceptual Definition Theoretical Definition Real Definition Conceptual Definition Theoretical Definition Real Definition Operational Definition (How to measured?) Operational Definition (How to measured?) Generalized idea Communication Real world Hypothesis testing Time Space Context Time Space Context Test-1Test-2Test-n

56 Y X y1 y2 x1 x2 x3 y3 Formative Indicator Model Reflective Indicator Model     

57 22 22 33 33 11 11 X1 X2 X3 X4 X5 X6 X7 X8 X9 The Component Loading or the Structure/Pattern Coefficient Measured variables (Observed) / Indicators / Items Factor structure / Component / Dimensions / Unmeasured variables

58 22 22 33 33 11 11 X1 X2 X3 X4 X5 X6 X7 X8 The Factor Loading or the Structure/Pattern Coefficient                 Measured variables (Observed) / Indicators / Items Factor structure / Component / Dimensions / Unmeasured variables Errors or Uniqueness

59 Measured variables (Observed) / Indicators / Items 22 22 33 33 11 11 X1 X2 X3 X4 X5 X6 X7 X8 X9 The Factor Loading or the Structure/Pattern Coefficient Factor structure / Component / Dimensions / Unmeasured variables                   Errors or Uniqueness  2,1  3,1  3,2

60 22 22 33 33 11 11 X1 X2 X3 X4 X5 X6 X7 X8 X9 The Factor Loading or the Structure/Pattern Coefficient                    2,1  3,1  3,2 Some Errors are correlated 2,1 1,1 3,1 4,2 5,2 6,2 7,3 8,3 9,3 Measured variables (Observed) / Indicators / Items Factor structure / Component / Dimensions / Unmeasured variables Errors or Uniqueness

61 11 22 33 44 55 66 77 88 99  10 1111  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x24 x25 x26 x27 x28 x29 x30 x31 x32 x33 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

62 11 22 33 44 55 66 77 88 99  10 1111  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x24 x25 x26 x27 x28 x29 x30 x31 x32 x33 F-1 F-2 F-3 F-4 F-A F-B Second-order Confirmatory Factor Analytic Model

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64 M-1 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x24 x25 x26 x27 x28 x29 x30 x31 x32 x33 LV-1 LV-2 LV-3 LV-4 M-2

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67 Y Y X1 X2 X3 Causal Modeling I: Path Analysis with Observed Variables Y Y X1 X2 X5 X4 Total Effect = Direct + Indirect Effects X3

68 22  1,1  2,1  3,1 2222 2222 Y 6, 2 Y 4, 2 Y 5, 2 1111 1111 X 3, 1 X 1, 1 X 2, 1 2222 2222 X 6, 2 X 4, 2 X 5, 2 1111 1111 Y 3, 1 Y 1, 1 Y 2, 1 Causal Modeling II: Path Analysis with Latent Variables Linear Structural Equation Modeling (SEM)  4,2  1,1  5,2  6,3  2,1  3,1  4,2  5,2  6,2 11 Total Effect = Direct + Indirect Effects SEM = [Path Analysis + Confirmatory Factor Analysis]

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70 YY X1X1 X2X2 X3X3 Independent variables Dependent variables No Correlation (r = 0) Direct effects  y.x1  y.x2  y.x3 TI Regression Model DA NO=250 NI=4 MA=CM LA Y X1 X2 X3 KM ME SD MO NY=1 NX=3 GA=FU PH=SY PS=SY PA GA PA PH PD OU SE TV RS MR EF SS SC MI ND=3 AD=OFF

71 Y1Y1AA BB CC Independent variables Dependent variables No Correlation (r = 0) Direct effects  y1.x 1  y.x2  y1.x 3 TI Testing MMR DA NI=6 NO=320 MA=CM LA Y1 Y2 A B C D KM SY ME SD MO NX=4 NY=2 GA=FU PH=SY PS=SY PA GA 1 1 PA PH PA PS PD OU SE TV RS MR MI ND=3 AD=OFF DD  y.1x4 Y2Y2  y2.x 1  y2.x2  y.2x3  y2.x4

72 M1M1M1M1 M1M1M1M1 X1X1 X2X2 X3X3 Independent variables Dependent variables No Correlation (r = 0) Total effect = Direct effects + Indirect effect  M2M2M2M2 M2M2M2M2 YY X4X4 X5X5      

73 TI Path analysis DA NI=8 NO=320 MA=KM LA M1 M2 Y X1 X2 X3 X4 X5 KM SY MO NX=5 NY=3 GA=FU BE=SD PH=SY PS=SY PA GA PA BE PA PH PA PS PD OU SE TV RS MR EF SC MI=OFF ND=2

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