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
Formulate hypotheses; operationaliz e variables; examine distributiona l assumption
Item analysis; reliability analysis principal Componen t analysis;
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
Formulate hypotheses; operationaliz e variables; examine distributiona l assumption
Item analysis; reliability analysis principal Componen t analysis;
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