Module 5: t-Test and SEM Intro

Module 5:Module 5:

t-Test and SEM Introt-Test and SEM IntroRosseni DinRosseni Din

Muhammad Faisal Kamarul ZamanMuhammad Faisal Kamarul ZamanNurainshah Abdul MutalibNurainshah Abdul Mutalib

TypesTypes Independent-samplesIndependent-samples

• Compare mean scores of 2 different groupsCompare mean scores of 2 different groups

Paired-samplesPaired-samples• Compare mean of the same group on 2 different Compare mean of the same group on 2 different

occasionsoccasions

Only comparing 2 groups or 2 conditionsOnly comparing 2 groups or 2 conditions

More than that use varianceMore than that use variance

IndependantIndependant It needsIt needs

• One categorical variable / independent variableOne categorical variable / independent variable• One continuous variable / dependant variableOne continuous variable / dependant variable

What the test will doWhat the test will do• It will tell you whether there is a statistically It will tell you whether there is a statistically

significant difference in the mean scores for significant difference in the mean scores for the 2 groups.the 2 groups.

Assumptions neededAssumptions needed

PairedPaired One group but 2 different occasion / One group but 2 different occasion /

conditionsconditions• E.g. pre/post testE.g. pre/post test

Requirements: the same as independentRequirements: the same as independent• One categorical independentOne categorical independent• One continuous, dependent variableOne continuous, dependent variable

It will tell you whether there is a statistically It will tell you whether there is a statistically significant in the mean scores significant in the mean scores

Data Analysis Using Data Analysis Using SPSSSPSS

t-testt-test

t-testt-test Used to test whether there is Used to test whether there is

significant difference between the significant difference between the means of two groups, e.g.:means of two groups, e.g.:• Male v female Male v female • Full-time v part-time Full-time v part-time

t-testt-test Typical hypotheses for t-test:Typical hypotheses for t-test:

a)a) There is no difference in affective commitment There is no difference in affective commitment (affcomm) between male and female employees(affcomm) between male and female employees

b)b) There is no difference in continuance There is no difference in continuance commitment (concomm) between male and commitment (concomm) between male and female employeesfemale employees

c)c) There is no difference in normative commitment There is no difference in normative commitment (norcomm) between male and female employees(norcomm) between male and female employees

Performing T-testPerforming T-test

Analyze Analyze → → Compare Means → Compare Means →

Independent-Samples T-testIndependent-Samples T-test

Compare Means

Analyze

Independent-Samples T Test

Performing T-testPerforming T-test Select the variables to test (Test Select the variables to test (Test

Variables), in this case:Variables), in this case:• affcommaffcomm• concommconcomm• norcommnorcomm

And bring the variables to the “Test And bring the variables to the “Test Variables” boxVariables” box

Test variables are selected and carried to the box on the right by pressing the arrow

The test variables: affcomm, concomm, and norcomm

Performing T-testPerforming T-test Select the grouping variable, i.e. Select the grouping variable, i.e.

gender; bring it to the “grouping gender; bring it to the “grouping variable” boxvariable” box

Click “Define Groups”Click “Define Groups”

Gender is the grouping variable

Performing T-testPerforming T-test Choose “Use specified values”Choose “Use specified values” Key in the codes for the variable Key in the codes for the variable

“gender” as used in the “Value “gender” as used in the “Value Labels”. In this case:Labels”. In this case:1 - Male1 - Male2 - Female 2 - Female

Click “Continue”, then “OK”Click “Continue”, then “OK”

Specified values for gender are: 1 (Male) and 2 (Female)

T-Test: SPSS Output

Group Statistics

357 3.49720 .731988 .038741315 3.38016 .696273 .039231357 3.18838 .756794 .040054315 3.15159 .666338 .037544357 3.24090 .665938 .035245315 3.27540 .647409 .036477

GENDER OFRESPONDENTMALEFEMALEMALEFEMALEMALEFEMALE

affcomm

concomm

norcomm

N Mean Std. DeviationStd. Error

Mean

Mean scores for “Male” on the three test variables

The mean scores for “Female”

Independent Samples Test

1.048 .306 2.116 670 .035 .117040 .055308 .008442 .225638

2.123 666.213 .034 .117040 .055135 .008780 .225300

5.353 .021 .665 670 .506 .036788 .055335 -.071863 .145440

.670 669.997 .503 .036788 .054899 -.071006 .144582

.656 .418 -.679 670 .497 -.034500 .050813 -.134272 .065271

-.680 663.726 .497 -.034500 .050723 -.134097 .065096

Equal variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumed

affcomm

concomm

norcomm

F Sig.

Levene's Test forEquality of Variances

t df Sig. (2-tailed)Mean

DifferenceStd. ErrorDifference Lower Upper

95% ConfidenceInterval of the

Difference

t-test for Equality of Means

T-test: SPSS Output

(1) Sig. is 0.306 (> 0.05) so there is no significant difference in the variances of the two groups(2) so the row “Equal variances assumed” will be used to read the sig. of t-test(3) Sig. level for t-test is 0.035 (<0.05)

Therefore there is a significant difference in the levels of affective commitment (affcomm) between male and female employees.

12

3

From the SPSS output, we are able From the SPSS output, we are able to see that the means of the to see that the means of the respective variables for the two respective variables for the two groups are:groups are:

• Affective commitment (affcomm) Affective commitment (affcomm) Male 3.49720 Female 3.38016Male 3.49720 Female 3.38016

• Continuance commitment (concomm)Continuance commitment (concomm) Male 3.18838 Female 3.15159Male 3.18838 Female 3.15159

• Normative commitment (norcomm)Normative commitment (norcomm) Male 3.24090 Female 3.27540Male 3.24090 Female 3.27540

T-test: InterpretationT-test: Interpretation For the variable “affcomm”For the variable “affcomm”

• Levene’s Test for Equality of Variances Levene’s Test for Equality of Variances shows that F (1.048) is not significant shows that F (1.048) is not significant (0.306)* therefore the “Equal variances (0.306)* therefore the “Equal variances assumed” row will be used for the t-test.assumed” row will be used for the t-test.

* This score (sig.) has to be 0.05 or less to be * This score (sig.) has to be 0.05 or less to be considered significant.considered significant.

T-test: InterpretationT-test: Interpretation

Under the “t-test for Equality of Under the “t-test for Equality of Means” look at “Sig. (2-tailed)” for Means” look at “Sig. (2-tailed)” for “Equal variances assumed”. “Equal variances assumed”.

The score is 0.035 (which is less than The score is 0.035 (which is less than 0.05), therefore there is a significant 0.05), therefore there is a significant difference between the means of the difference between the means of the two groups.two groups.


1.048 .306 2.116 670 .035 .117040 .055308 .008442 .225638

2.123 666.213 .034 .117040 .055135 .008780 .225300

5.353 .021 .665 670 .506 .036788 .055335 -.071863 .145440

.670 669.997 .503 .036788 .054899 -.071006 .144582

.656 .418 -.679 670 .497 -.034500 .050813 -.134272 .065271

-.680 663.726 .497 -.034500 .050723 -.134097 .065096


affcomm

concomm

norcomm

F Sig.





Difference


T-test: Interpretation

1. Sig. is 0.021 (<0.05), there is significant difference between the variances2. The row “Equal variances not assumed” is used for interpreting the t-test3. The relevant significant level for t-test is 0.503 (>0.05)

Therefore, there is no significant difference between the two groups

2 31

T-test: InterpretationT-test: Interpretation For the variable “concomm”For the variable “concomm”

• Levene’s Test for Equality of Variances Levene’s Test for Equality of Variances shows that F (5.353) is significant shows that F (5.353) is significant (0.021)* therefore the “Equal variances (0.021)* therefore the “Equal variances not assumed” row will be used for the t-not assumed” row will be used for the t-test.test.

* This score (sig.) is less than 0.05, so there is * This score (sig.) is less than 0.05, so there is significant different in the variances of the significant different in the variances of the two groups.two groups.

T-test: InterpretationT-test: Interpretation Under the “t-test for Equality of Under the “t-test for Equality of

Means” look at “Sig. (2-tailed)” for Means” look at “Sig. (2-tailed)” for “Equal variances not assumed”. “Equal variances not assumed”.

The score is 0.503 (which is more The score is 0.503 (which is more than 0.05), therefore there is no than 0.05), therefore there is no significant difference between the significant difference between the means of the two groups.means of the two groups.


1.048 .306 2.116 670 .035 .117040 .055308 .008442 .225638

2.123 666.213 .034 .117040 .055135 .008780 .225300

5.353 .021 .665 670 .506 .036788 .055335 -.071863 .145440

.670 669.997 .503 .036788 .054899 -.071006 .144582

.656 .418 -.679 670 .497 -.034500 .050813 -.134272 .065271

-.680 663.726 .497 -.034500 .050723 -.134097 .065096


affcomm

concomm

norcomm

F Sig.





Difference


T-test: Interpretation

1

1. The sig. is 0.418 (>0.05) so there is no significant difference between the variances2. “Equal variances assumed” will be used to determine t-test3. The Sig. of t-test is 0.497 (>0.05)

Therefore there is no significant difference between the means of the two groups

2

T-test: InterpretationT-test: Interpretation For the variable “norcomm”For the variable “norcomm”

• Levene’s Test for Equality of Variances Levene’s Test for Equality of Variances shows that F (0.656) is not significant shows that F (0.656) is not significant (0.418)* therefore the “Equal variances (0.418)* therefore the “Equal variances are assumed” row will be used for the t-are assumed” row will be used for the t-test.test.

* This score (sig.) is more than 0.05, so there * This score (sig.) is more than 0.05, so there is no significant different in the variances of is no significant different in the variances of the two groups.the two groups.

T-test: InterpretationT-test: Interpretation Under the “t-test for Equality of Under the “t-test for Equality of

Means” look at “Sig. (2-tailed)” for Means” look at “Sig. (2-tailed)” for “Equal variances assumed”. “Equal variances assumed”.

The score is 0.497 (which is more The score is 0.497 (which is more than 0.05), therefore there is no than 0.05), therefore there is no significant difference between the significant difference between the means of the two groups.means of the two groups.

Hands-on exerciseHands-on exercise Use survey3ED.sav from Use survey3ED.sav from

www.allenandunwin.com/spss

OROR

http://rosseni.wordpress.com/2011/07/15/spss-for-beginners/

http://www.allenandunwin.com/spss



Procedure for independent-sample t-testProcedure for independent-sample t-test

1. Analyze > Compare means > independent 1. Analyze > Compare means > independent samples t-testsamples t-test

2. Move the dependent (continuos) variable (e.g. 2. Move the dependent (continuos) variable (e.g. total self-esteemtotal self-esteem) > ) > Test Variable BoxTest Variable Box

3. Move the independent (categorical) variable (e.g. 3. Move the independent (categorical) variable (e.g. sexsex) > ) > Grouping VariableGrouping Variable

Procedure for independent-sample t-testProcedure for independent-sample t-test

4. Click 4. Click define groupsdefine groups > type in the numbers used in the > type in the numbers used in the data set to code each group. In the curent data file, data set to code each group. In the curent data file, 1=males, 2=females; therefore, in the 1=males, 2=females; therefore, in the Group 1 Group 1 box type box type 1; 1; Group 2 Group 2 box type 2;box type 2;

* if you cannot remember the codes used, right click on the variable name * if you cannot remember the codes used, right click on the variable name and then choose Variable Information from the pop-up box that appears. and then choose Variable Information from the pop-up box that appears. This will list the codes and labelsThis will list the codes and labels

5. Click 5. Click continue continue > > okok

Intro to SEMIntro to SEM

Structural Equation ModelingStructural Equation Modeling

Purpose of the StudyPurpose of the Study

The study The study development of a model development of a model for meaningful e-Training by blending for meaningful e-Training by blending conventional and computer mediated conventional and computer mediated communication to cater to learners communication to cater to learners with differentiated LS preferences. In with differentiated LS preferences. In this study we call it the this study we call it the Hybrid Hybrid eTraining eTraining method.method.

The Extension: Conceptual Framework of a Hybrid E-Training System (HiTs)

Develop, implement and evaluate Develop, implement and evaluate a hybrid system implementation a hybrid system implementation

that caters learners with that caters learners with differentiated learning style differentiated learning style

preferences, achieve meaningful preferences, achieve meaningful learninglearning

Overall Research Overall Research FrameworkFramework

Overall Research Framework

Learning Style Preference

(LSP)Tactual

Group

Individual Kinesthetic

Auditory

Visual

Content

Delivery

Structure

Service

Outcome

Hybride-Training

(HiTs)

Meaningfule-Training

(MeT)

Cooperativity

Intentionality

Construction

Activity

Authenticity

n = 213 ICT trainers/trainees studying as postgraduate students/graduating fourth

year students participated in the Technology for Thinking/Computer Education course in the year 2008

Modeling Procedures FF analysis Prelim Analysis

Formulatehypotheses; operationalize variables;

examine distributiona

lassumption

Item analysis;reliabilityanalysisprincipal

Component analysis;

Validate measurement

models: HiT model specification; estimation; fit

assessment; path adequacy; SMC

Validate other CFA models : MeT and LSP model specification;

estimation; fit assessment;

path adequacy; SMC

Test structural model: (1) HiT(1) HiT MeT MeT and (2) LSP ) LSP HiT HiT model

specifications; estimation; fit assessment; path adequacy; SMC

Confirmatory Modeling Strategy

1a 1b 2a 2b

2c

3

Overview of the Analytical Approach

Test the full-

fledged

model

Hypothesized HiT in relation to MeT and LSP . . . Hypothesized HiT in relation to MeT and LSP . . .

MeT

coop e61

1

inten e71

const e81

activ e91

authen e101

HiTs

outcme5

serve4

struce3

delivere2

contente1

1

1

1

1

1

1 e17

1

LSP

group

e15

tactil

e14

kines

e13

audio

e12

visual

e11

1

11111

indiv

e16

1

e181

e19

1

Reliability of the InstrumentsReliability of the Instruments

Meaningful e-Training (MeT) Measure Meaningful e-Training (MeT) Measure αα ==.89.89

Hybrid E-Training (HiT) Measure α =.93

Learning Style Preference (LSP) α =.88

HiT Measurement ModelHiT Measurement Model

HiTs

serve2

struce3

delivere4

contente5

Normed Chi-Square 3.155RMSEA .101CFI .993TLI .975p .024

.82

.89

.77 .79

outcme1

.95

.39

-.52

MeT Measurement ModelMeT Measurement Model

MeT

inten.74

const.85

activ

.83

authen.95


e16coop e4

e6

e5

e7

e8

.52

-1.01

LSP Measurement ModelNormed Chi-Square 1.249RMSEA .034CFI .998TLI .994p .288

LSP

tactil

e16

kines

e14

visual

e13

audio

e12

group

e15

.74 .62.66 .85.52

.57

Structural Relationship of HiTStructural Relationship of HiTMeTMeT

MeT

coop e6.52

inten e7.74

const e8.84

activ e9

.83

authen e10

.96

HiTs

outcme5

serve4

struce3

delivere2

contente1


.89

.86

.85

.81

.80

.45

e16

.35

-.24

.41

-.08

-1.06

Structural Relationship of Structural Relationship of LSPLSPHiTsHiTs

HiTs

outcme5

serve4

struce3

delivere2

contente1


.93

.82

.91

.80

.72

LSP

group e15

tactil e14

kines e13

audio e12

visual e11

.63

.52

.84

.74.66

.57

-.34.25

.46

.18

e16

CoverageCoverage

I. Statement of problemI. Statement of problem Objectives of the studyObjectives of the study Extension of the current hybrid modelExtension of the current hybrid model

II. MethodII. Method Setting; sample; Setting; sample; Modeling procedure Modeling procedure

III. ResultsIII. Results Measurement modelMeasurement model Structural modelStructural model Full-fledged modelFull-fledged model

IV. ConclusionIV. Conclusion

Modeling ProceduresModeling Procedures FF analysis FF analysis Prelim AnalysisPrelim Analysis

FormulateFormulatehypotheses; hypotheses; operationalioperationalize variables;ze variables;

examine examine distributionadistributiona

llassumption assumption

Item Item analysis;analysis;reliabilityreliabilityanalysisanalysisprincipalprincipal

ComponenComponent analysis;t analysis;

Validate Validate measurement measurement

models: HiT model models: HiT model specification; specification; estimation; fit estimation; fit

assessment; path assessment; path adequacy; SMCadequacy; SMC

Validate other CFA Validate other CFA models : MeT and LSP models : MeT and LSP model specification; model specification;

estimation; fit estimation; fit assessment;assessment;

path adequacy; SMC path adequacy; SMC

Test structural model: Test structural model: (1) HiT(1) HiT MeT and (2) LSP MeT and (2) LSP HiT model HiT model

specifications; estimation; fit assessment;specifications; estimation; fit assessment; path adequacy; SMC path adequacy; SMC

Confirmatory Modeling Strategy Confirmatory Modeling Strategy

1a1a 1b1b 2a2a 2b2b

2c2c

33

Overview of the Analytical ApproachOverview of the Analytical Approach

Test Test the the full-full-

fledgefledged d

modelmodel

Adequacy of the full fledge Integrated Meaningful Hybrid E-Training (I-MeT) Model Adequacy of the full fledge Integrated Meaningful Hybrid E-Training (I-MeT) Model

MeT

coop e1.52

inten e2.75

const e3.84

activ e4

.84

authen e5

.95

HiTs

outcme11

serve12

struce13

delivere14

contente15


.89

.87

.84

.80

.79

LSP

Group

e6

tactil

e7

kines

e8

visual

e9

audio

e10

.62.51.84.67.75

.49

-.25

e16

.57

-.95.43

-.25

.37

.15

e17

Module 5: t-Test and SEM Intro

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