Data Analysis Using SPSS

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Transcript Data Analysis Using SPSS

Module 5:

t-Test and SEM Intro

Rosseni Din Muhammad Faisal Kamarul Zaman Nurainshah Abdul Mutalib

Types

 Independent-samples • Compare mean scores of 2 different groups  Paired-samples • Compare mean of the same group on 2 different occasions  Only comparing 2 groups or 2 conditions  More than that use variance

Independant

 It needs • One categorical variable / independent variable • One continuous variable / dependant variable  What the test will do • It will tell you whether there is a statistically significant difference in the mean scores for the 2 groups.

 Assumptions needed

Paired

 One group but 2 different occasion / conditions • E.g. pre/post test  Requirements: the same as independent • One categorical independent • One continuous, dependent variable  It will tell you whether there is a statistically significant in the mean scores

Data Analysis Using SPSS

t-test

t-test

 Used to test whether there is significant difference between the means of two groups, e.g.: • Male v female • Full-time v part-time

t-test

 Typical hypotheses for t-test:

a) There is no female employees difference in affective commitment (affcomm) between male and b) There is no difference female employees in continuance commitment (concomm) between male and c) There is no female employees difference in normative commitment (norcomm) between male and

Performing T-test

Analyze → Compare Means → Independent-Samples T-test

Analyze Compare Means

Independent-Samples T Test

Performing T-test

 Select the variables to test (Test Variables), in this case: • affcomm • concomm • norcomm  And bring the variables to the “Test Variables” 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-test

 Select the grouping variable, i.e.

gender; bring it to the “grouping variable” box  Click “Define Groups”

Gender is the grouping variable

Performing T-test

  Choose “Use specified values” Key in the codes for the variable “gender” as used in the “Value Labels”. In this case: 1 - Male 2 - Female  Click “Continue”, then “OK”

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

T-Test: SPSS Output

affcomm concomm norcomm GENDER OF RESPONDENT MALE FEMALE MALE FEMALE MALE FEMALE

Group Statistics

N 357 315 357 315 357 315 Mean 3.49720

3.38016

3.18838

3.15159

3.24090

3.27540

Std. Deviation .731988

.696273

.756794

.666338

.665938

.647409

Std. Error Mean .038741

.039231

.040054

.037544

.035245

.036477

Mean scores for “Male” on the three test variables The mean scores for “Female”

affcomm concomm norcomm Equal variances ass umed Equal variances not as sumed Equal variances ass umed Equal variances not as sumed Equal variances ass umed Equal variances not as sumed Levene's Test for Equality of Variances F 1.048

5.353

.656

Sig.

.306

.021

.418

T-test: SPSS Output

Independent Samples Test

t 2.116

2.123

.665

.670

-.679

-.680

t-tes t for Equality of Means df 670 Sig. (2-tailed) .035

Mean Difference .117040

666.213

670 .034

.506

.117040

.036788

Std. Error Difference .055308

.055135

.055335

95% Confidence Interval of the Difference Lower Upper .008442

.225638

.008780

-.071863

.225300

.145440

669.997

670 663.726

.503

.497

.497

.036788

-.034500

-.034500

.054899

-.071006

.050813

-.134272

.050723

-.134097

.144582

.065271

.065096

2 1 3 (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.

 From the SPSS output, we are able to see that the means of the respective variables for the two groups are: • Affective commitment (affcomm)  Male 3.49720 Female 3.38016

• Continuance commitment (concomm)  Male 3.18838 Female 3.15159

• Normative commitment (norcomm)  Male 3.24090 Female 3.27540

T-test: Interpretation

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

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

T-test: Interpretation

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

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

T-test: Interpretation affcomm concomm norcomm Equal variances ass umed Equal variances not as sumed Equal variances ass umed Equal variances not as sumed Equal variances ass umed Equal variances not as sumed Levene's Test for Equality of Variances F 1.048

5.353

.656

Sig.

.306

.021

.418

Independent Samples Test

t 2.116

2.123

.665

.670

-.679

-.680

t-tes t for Equality of Means df 670 Sig. (2-tailed) .035

Mean Difference .117040

666.213

670 .034

.506

.117040

.036788

Std. Error Difference .055308

.055135

.055335

95% Confidence Interval of the Difference Lower Upper .008442

.225638

.008780

-.071863

.225300

.145440

669.997

670 663.726

.503

.497

.497

.036788

-.034500

-.034500

.054899

-.071006

.050813

-.134272

.050723

-.134097

.144582

.065271

.065096

2 1 3 1. Sig. is 0.021 (<0.05), there is significant difference between the variances 2. The row “Equal variances not assumed” is used for interpreting the t-test 3. The relevant significant level for t-test is 0.503 (>0.05) Therefore, there is no significant difference between the two groups

T-test: Interpretation

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

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

T-test: Interpretation

 Under the “t-test for Equality of Means” look at “Sig. (2-tailed)” for “Equal variances not assumed”.

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

T-test: Interpretation

Independent Samples Test

Levene's Test for Equality of Variances t-tes t for Equality of Means affcomm concomm norcomm Equal variances ass umed Equal variances not as sumed Equal variances ass umed Equal variances not as sumed Equal variances ass umed Equal variances not as sumed 2 F 1.048

5.353

.656

Sig.

.306

.021

.418

t 2.116

2.123

.665

.670

-.679

-.680

df 670 Sig. (2-tailed) .035

Mean Difference .117040

Std. Error Difference .055308

666.213

670 669.997

670 663.726

.034

.506

.503

.497

.497

.117040

.036788

.036788

-.034500

-.034500

.055135

.055335

.054899

.050813

.050723

95% Confidence Interval of the Difference Lower Upper .008442

.225638

.008780

-.071863

-.071006

-.134272

-.134097

.225300

.145440

.144582

.065271

.065096

1 1. The sig. is 0.418 (>0.05) so there is no significant difference between the variances 2. “Equal variances assumed” will be used to determine t-test 3. The Sig. of t-test is 0.497 (>0.05) Therefore there is no significant difference between the means of the two groups

T-test: Interpretation

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

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

T-test: Interpretation

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

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

Hands-on exercise

 Use survey3ED.sav from www.allenandunwin.com/spss  OR  http://rosseni.wordpress.com/2011/ 07/15/spss-for-beginners/

Procedure for independent-sample t-test 1. Analyze > Compare means > independent samples t-test 2. Move the dependent (continuos) variable (e.g. total self-esteem ) > Test Variable Box 3. Move the independent (categorical) variable (e.g. sex ) > Grouping Variable

Procedure for independent-sample t-test 4. Click define groups > type in the numbers used in the data set to code each group. In the curent data file, 1=males, 2=females; therefore, in the Group 1 box type 1; Group 2 box type 2;  * 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. This will list the codes and labels 5. Click continue > ok

Intro to SEM

Structural Equation Modeling

Purpose of the Study

The study

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

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

Overall Research Framework

Develop, implement and evaluate a hybrid system implementation that caters learners with differentiated learning style preferences, achieve meaningful learning

Content Delivery Service Structure Outcome Overall Research Framework

Hybrid e-Training (HiTs) Meaningful e-Training (MeT)

Cooperativity Intentionality Construction Activity Authenticity Individual Auditory

Learning Style Preference (LSP)

Visual Group Kinesthetic Tactual

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

Overview of the Analytical Approach Prelim Analysis Modeling Procedures FF analysis

1a

1b

Formulate hypotheses; operationaliz e variables; examine distributiona l assumption

Item analysis; reliability analysis principal Componen t analysis;

2a Validate measurement models: HiT model specification; estimation; fit assessment; path adequacy; SMC

2b Validate other CFA models : MeT and LSP model specification; estimation; fit assessment; path adequacy; SMC

2c (1) HiT  Test structural model: MeT and (2) LSP  HiT path adequacy; SMC model specifications; estimation; fit assessment;

3 Test the full fledge d model

Confirmatory Modeling Strategy

Hypothesized HiT in relation to MeT and LSP . . .

e1 e2 e3 e4 e5 1 1 1 1 1 content deliver struc serv outcm 1 e19 1 HiTs e18 1 LSP e17 1 MeT 1 coop inten const activ authen 1 e6 1 e7 1 e8 1 e9 1 e10 1 visual 1 audio 1 kines 1 tactil 1 group 1 indiv 1 e11 e12 e13 e14 e15 e16

Reliability of the Instruments

Meaningful e-Training (MeT) Measure 

α =.89

Hybrid E-Training (HiT) Measure 

α =.93

Learning Style Preference (LSP) 

α =.88

HiT Measurement Model

Normed Chi-Square 3.155

RMSEA .101

CFI .993

TLI .975

p .024

-.52

.39

e5 e4 e3 e2 e1 content deliver .77

struc .89

.82

serv .95

.79

outcm HiTs

MeT Measurement Model Normed Chi-Square 1.095

RMSEA .021

CFI .999

TLI .998

p .357

LSP Measurement Model Normed Chi-Square 1.249

RMSEA .034

CFI .998

TLI .994

p .288

e16 MeT .95

.52

coop .74

inten .85

.83

const activ authen e4 e5 e6 e7 e8 -1.01

.74

.66

LSP .85

.62

.52

audio visual kines group tactil e12 e13 e14 e15 e16 .57

Structural Relationship of HiT

MeT

Normed Chi-Square 2.509

RMSEA .084

CFI .972

TLI .956

p .000

-.24

e1 .35

-.08

e2 e3 .41

e4 e5 content deliver .85

struc .86

serv .89

.80

.81

outcm HiTs .45

e16 MeT coop .52

.74

.84

inten .83

const .96

activ authen e6 e7 e8 e9 -1.06

e10

e1 .46

e2 .25

-.34

e3 e4 e5

Structural Relationship of LSP

HiTs

Normed Chi-Square 2.603

RMSEA .087

CFI .964

TLI .946

p .000

content deliver .91

struc .82

.72

.80

serv .93

outcm HiTs e16 .18

LSP group .63

.52

tactil .84

.74

.66

kines audio visual e15 .57

e14 e13 e12 e11

Coverage

I. Statement of problem   Objectives of the study Extension of the current hybrid model II. Method   Setting; sample; Modeling procedure III. Results    Measurement model Structural model Full-fledged model IV. Conclusion

Overview of the Analytical Approach Prelim Analysis Modeling Procedures FF analysis

1a

1b

Formulate hypotheses; operationaliz e variables; examine distributiona l assumption

Item analysis; reliability analysis principal Componen t analysis;

2a Validate measurement models: HiT model specification; estimation; fit assessment; path adequacy; SMC

2b Validate other CFA models : MeT and LSP model specification; estimation; fit assessment; path adequacy; SMC

2c (1) HiT  Test structural model: MeT and (2) LSP  HiT path adequacy; SMC model specifications; estimation; fit assessment;

3 Test the full fledge d model

Confirmatory Modeling Strategy

Adequacy of the full fledge Integrated Meaningful Hybrid E-Training (I-MeT) Model Normed Chi-Square 2.394

RMSEA .081

CFI .945

TLI .929

p .000

e17 e16 -.25

.37

e15 e14 e13 .43

e12 e11 content deliver struc serv .84

.87

.89

.79

.80

outcm HiTs .15

.49

-.25

MeT coop .52

.75

.84

inten .84

.95

const activ authen e1 e2 e3 e4 e5 .75

.67

LSP .84

.51

.62

-.95

audio visual kines tactil Group e10 e9 e8 e7 .57

e6