¶ … High School Longitudinal Study Dataset Using SPSS Software
Scenarios 1.
This section determines African citizen's perceptions about their current level of democracy. The study uses 2015 Afrobarometer data to determine whether the current level of African democracy is statistically different from the value of 6.
Have series of reforms increased African views on the level of democracy?
The study uses the following variables to answer the research question:
Level of democracy: ten years ago (0-10 scale)
Level of democracy: today
The paper uses One-Sample Test statistics to answer the research question. The procedure is as follows:
In the SPSS Version 21, Start by Clicking:
Analyze ? Compare Means ?One Sample T Test
In Test Variable box:
Select
Q46b. Level of democracy ten years ago
Q46a. Level of democracy: today and Click OK.
The output of the One Sample T Test is as follows:
GET
FILE='C:UsersucerDownloads183172_Afrobarometer__student_8210__.sav'.
DATASET NAME DataSet1 WINDOW=FRONT.
T-TEST
/TESTVAL=0
/MISSING=ANALYSIS
/VARIABLES=Q46A Q46B
/CRITERIA=CI (.95).
T-Test
Notes
Output Created
11-JUL-2016 09:41:20
Comments
Input
Data
C:UsersucerDownloads183172_Afrobarometer__student_8210__.sav
Active Dataset
DataSet1
Filter
one>
Weight
one>
Split File
one>
N of Rows in Working Data File
51587
Missing Value Handling
Definition of Missing
User defined missing values are treated as missing.
Cases Used
Statistics for each analysis are based on the cases with no missing or out-of-range data for any variable in the analysis.
Syntax
T-TEST
/TESTVAL=0
/MISSING=ANALYSIS
/VARIABLES=Q46A Q46B
/CRITERIA=CI (.95).
Resources
Processor Time
00:00:00,06
Elapsed Time
00:00:00,06
[DataSet1] C:UsersucerDownloads183172_Afrobarometer__student_8210__.sav
One-Sample Statistics
N
Mean
Std. Deviation
Std. Error Mean
Q46a. Level of democracy: today
46940
5,52
2,883
,013
Q46b. Level of democracy: ten years ago (0-10 scale)
44909
4,90
2,995
,014
One-Sample Test
Test Value = 0
t df
Sig. (2-tailed)
Mean Difference
95% Confidence Interval of the Difference
Lower
Upper
Q46a. Level of democracy: today
415,007
46939
,000
5,522
5,50
5,55
Q46b. Level of democracy: ten years ago (0-10 scale)
346,461
44908
,000
4,896
4,87
4,92
As being revealed in the SPSS output, the Mean value of the Level of democracy 10 years is 4.90 while the mean value of the Level of Democracy today is 5.52. Thus, the scale Level of Democracy today (5.52) is better than Level of Democracy in the last 10 years (4.90) in the African countries. The result reveals that level of democracy today in Africa has improved compared with the level of democracy 10 years ago. The major reasons for the improvement in the democracy include globalization, and the internet that lead to the transparency of African governments.
Scenarios 2
The recent social changes in North Africa make this study to determine whether there is a statistical difference in people's perceptions of Southern Africa and North Africa.
Research Question:
Is there a difference in people's perceptions about Southern Africa and North Africa democratic reform?
The study uses the Paired Samples T Test to determine the statistical difference between North Africa and Southern Africa.
The variables used to answer the research question is as follows:
Q47c. Level of democracy: South Africa (0-10 scale)
Country by region
Since the country of region consists of four regions (West Africa, East African, South African and North Africa), and the paper only wish to study the North African region, the study deletes West Africa, East African, and South Africa, which remains only North Africa.
How to delete.
Click Country of Region in the Values box.
Click West Africa, Click Remove,
Click East Africa, Click Remove,
Click South Africa, Click Remove.
Do not remove North Africa, then Click OK.
Thus, the paper uses Paired-Sample Test statistics to answer the research question. The procedure is as follows:
In the SPSS Version 21, Start by Clicking:
Analyze ? Compare Means ?Paired Sample T Test
Then Select
Country by region
Q47c. Level of democracy: South Africa (0-10 scale)
Click OK.
The output of the test is revealed below:
T-TEST PAIRS=COUNTRY.BY.REGION WITH Q47C (PAIRED)
/CRITERIA=CI (.9500)
/MISSING=ANALYSIS.
T-Test
Notes
Output Created
11-JUL-2016 10:25:47
Comments
Input
Data
C:UsersucerDownloads183172_Afrobarometer__student_8210__.sav
Active Dataset
DataSet1
Filter
one>
Weight
one>
Split File
one>
N of Rows in Working Data File
51587
Missing Value Handling
Definition of Missing
User defined missing values are treated as missing.
Cases Used
Statistics for each analysis are based on the cases with no missing or out-of-range data for any variable in the analysis.
Syntax
T-TEST PAIRS=COUNTRY.BY.REGION WITH Q47C (PAIRED)
/CRITERIA=CI (.9500)
/MISSING=ANALYSIS.
Resources
Processor Time
00:00:00,11
Elapsed Time
00:00:00,15
[DataSet1] C:UsersucerDownloads183172_Afrobarometer__student_8210__.sav
Paired Samples Statistics
Mean
N
Std. Deviation
Std. Error Mean
Pair 1
Country by region
2,23
25222
1,084
,007
Q47c. Level of democracy: South Africa (0-10 scale)
6,58
25222
2,674
,017
Paired Samples Correlations
N
Correlation
Sig.
Pair 1
Country by region & Q47c. Level of democracy: South Africa (0-10 scale)
25222
-,074
,000
Paired Samples Test
Paired Differences
t df
Sig. (2-tailed)
Mean
Std. Deviation
Std. Error Mean
95% Confidence Interval of the Difference
Lower
Upper
Pair 1
Country by region - Q47c. Level of democracy: South Africa (0-10 scale)
-4,348
2,959
,019
-4,385
-4,311
-233,356
25221
,000
The output of the statistics makes the study affirms that there is a difference in people's perceptions about Southern Africa and North Africa democratic reform showing that South Africa democratic reforms have improved more than the North Africa democratic reform. The Mean value of Level of democracy of South Africa is 6.58 which is greater than the Mean value of Country by region which is 2.3.
Scenario 3
This section investigates the perception of high school students on whether the mathematical utility changes between their freshman year and senior year. The study uses the Independent Sample T-Test to understand whether high school student's perceptions about mathematical utility changed between their freshman and senior year.
Hypothesis: Perceptions of high school students show that mathematical utility changes between their freshman year and senior year.
The study choses two variables selected from the High School Longitudinal Study dataset to test the hypothesis and the chosen variables are as follows:
T1 Scale of Student's Mathematical Utility
T1 Student's sex.
The procedure is as follows:
In the SPSS Version 21, Start by Clicking:
Analyze ? Compare Means ?Independent Sample T Test
Put T1 Scale of Student's Mathematical Utility in the Test Variable box
Put T1 Student's sex in the Grouping Variable box
Click Define Variable and input 1 in Group 1 and input 2 in Group 2
Click Continue, and Click OK.
The output of the Independent Sample T Test is as follows:
T-TEST GROUPS=X1SEX (1-2)
/MISSING=ANALYSIS
/VARIABLES=X1MTHUTI
/CRITERIA=CI (.95).
T-Test
Notes
Output Created
11-JUL-2016 14:31:58
Comments
Input
Data
C:UsersucerDocuments183172_HS_Long_Study__student_.sav
Active Dataset
DataSet2
Filter
one>
Weight
one>
Split File
one>
N of Rows in Working Data File
23503
Missing Value Handling
Definition of Missing
User defined missing values are treated as missing.
Cases Used
Statistics for each analysis are based on the cases with no missing or out-of-range data for any variable in the analysis.
Syntax
T-TEST GROUPS=X1SEX (1-2)
/MISSING=ANALYSIS
/VARIABLES=X1MTHUTI
/CRITERIA=CI (.95).
Resources
Processor Time
00:00:00,08
Elapsed Time
00:00:00,08
[DataSet2] C:UsersucerDocuments183172_HS_Long_Study__student_.sav
Group Statistics
T1 Student's sex
N
Mean
Std. Deviation
Std. Error Mean
T1 Scale of student's mathematics utility
Male
,0140
1,01962
,01049
Female
-,0481
,97291
,01006
Independent Samples Test
Levene's Test for Equality of Variances
T-test for Equality of Means
F
Sig.
t df
Sig. (2-tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference
Lower
Upper
T1 Scale of student's mathematics utility
Equal variances assumed
17,400
,000
4,276
18800
,000
,06216
,01454
,03367
,09066
Equal variances not assumed
4,277
18775,932
,000
,06216
,01453
,03367
,09065
The results differentiate the respond of male and female students. The Mean value of male student is 0.0140 while the Mean value of the female student respondent is -0.0481. The Mean value of the entire student is (0.0140+ (-0.481)
Mean Scale Value =-0.0341 The result on perception of student's mathematics utility is low because its Mean scale is negative.
Moreover, the significance (p value) of a Levene's test is .000 which is less than the ? level of .05. Thus, the study rejects the null hypothesis because the p is less than 0.05.
PART 11
This paper provides the critique of the article titled "Predictors of Prison-Based Treatment Outcomes: A Comparison of Men and Women Participants" (Messina, Burdon, Hagopian, et al. 2006 p 7).
The authors investigate "differences between men and women entering prison-based therapeutic community (TC) treatment and to explore the relationship of those differences to posttreatment outcomes (i.e., aftercare participation and reincarceration rates)." (Messina, Burdon, Hagopian, et al. 2006 p 7).
The data are collected from 4,164 men and 4,386 women across 16 prisons in California. The authors compare the prison based on TCs using the T-test and chi-square analyses. The T-Test is used to measure the therapeutic community among men and women because the TC has been identified as a way of substance abuse treatment. (Messina, Burdon, Hagopian). The T-test is appropriate for the study because it assists the authors to compare treatments of men and women in the California-based prison. The purpose of the t-test is to investigate the statistical significance between two group of sampled population. (Andersson, 2009).
In the article, the t-test is appropriate to carry out the test, and the authors collect data to measure the Intake Assessment (IA) among 4,386 women and 4,164 men in California prison. The author displays the entire characteristics of treatment by gender. Some of the data presented are revealed in table 1.
The results stand alone because the authors use tables and discussion to present the findings. For example, the research article presents the results by revealing the characteristics of the sample treatment based on gender in the tabular form assisting in enhancing a greater understanding of the difference of the type treatments that men and women undergo in the prison. Moreover, the results present the posttreatment outcomes based on gender revealing both similarities and differences in the posttreatment outcomes for men and women prison inmate. The limitation of the study is that the authors do not use the different predictors of treatment outcomes to display whether the instruments provide similar results.
Assignment II
Part 1
This section develops a research question to be answered with the One-Way ANOVA statistics.
Are the number of AP math courses offered are the highest in the AP courses offered?
The study collects data from the "High School Longitudinal Study dataset" to answer the research question. The variables used for the One-Way ANOVA test is as follows:
Number of AP math courses offered
Number of AP courses offered
The procedure is as follows:
In the SPSS Version 21, Start by Clicking:
Analyze ? Compare Means ?One Way ANOVA
Put Number of AP math courses offered in the Dependent List box
Put Number of AP courses offered in Factor box
Then Click Option, Click Descriptive, and Click Continue
Click OK.
The output of One-Way ANOVA is presented below:
ONEWAY C2NUMAPMATH BY C2NUMAP
/STATISTICS DESCRIPTIVES
/MISSING ANALYSIS.
Oneway
Notes
Output Created
11-JUL-2016 16:23:13
Comments
Input
Data
C:UsersucerDocuments183172_HS_Long_Study__student_.sav
Active Dataset
DataSet2
Filter
one>
Weight
one>
Split File
one>
N of Rows in Working Data File
23503
Missing Value Handling
Definition of Missing
User-defined missing values are treated as missing.
Cases Used
Statistics for each analysis are based on cases with no missing data for any variable in the analysis.
Syntax
ONEWAY C2NUMAPMATH BY C2NUMAP
/STATISTICS DESCRIPTIVES
/MISSING ANALYSIS.
Resources
Processor Time
00:00:00,05
Elapsed Time
00:00:00,06
[DataSet2] C:UsersucerDocuments183172_HS_Long_Study__student_.sav
Descriptives
Number of AP math courses offered
N
Mean
Std. Deviation
Std. Error
95% Confidence Interval for Mean
Minimum
Maximum
Lower Bound
Upper Bound
Number of AP math courses offered
Sum of Squares
df
Mean Square
F
Sig.
Between Groups
8481,974
22
385,544
880,968
,000
Within Groups
7367,624
16835
,438
Total
15849,598
16857
The table in the ANOVA analysis enhances a greater understanding of the significant difference between the group means. The results show that the significant level is 0.000 (p =.000), which is below 0.05. Thus, the results reveals that math is not the highest number of AP math courses offered in the AP courses. The policy makers should provide incentives to increase the math courses offered among students since math is one of the major subjects that enhances the economic growth.
Part 2
This section provides a critique of the research article titled "Revisiting the Study Habits and Performances in Math of Grade 7 Students: A Basis for A Proposed Enhancement Program" (Descargar, & Cardona, 2016). The authors use the one-way ANOVA to investigate the student habit in mathematics and examine whether the student's study habit enhances performances in mathematics. This paper believes that one-way ANOVA is a most appropriate choice because it assists the authors to establish that "students perceived study habits as a great factor in attaining excellent academic performance." (Descargar, & Cardona, 2016 p 77).
Despite the elegant way that the study employed in presenting the results, the authors did not present the data that has been used for the analysis, which has been the major shortcoming of the study. Although, the authors provide a brief profile of respondents, however, the data are not displayed in a tabular form. Moreover, the data are not detailed enough. Despite these shortcomings, the authors have been able to present the results on students' performances in mathematics, perception of teachers,, and mean difference in mathematics. (Descargar, & Cardona, 2016). While the authors do not present the results in a tabular form however, this study is able to interpret the results based on the results of the ANOVA tests.
Part 3
The bivariate regression establishes the relationship between two variables. The research question that can be answered with bivariate regression, and Pearson correlation is as follows:
Is the parent's level of education influence the employment status?
The variables used for the analysis is as follows:
T2 Parent 1: highest level of education
T2 Parent 1: employment status
The procedure is as follows:
In the SPSS Version 21, Start by Clicking:
Analyze ? Correlate ?Bivariate
Put the two variables in the Variables
Click Options
Select Means and Standard Deviation
Select Cross product devation and Covariance
Click Continue
Click Two-Tailed
Click Flag Significant Correlations
Click OK.
The output of the Bivariate is as follows:
CORRELATIONS
/VARIABLES=X2PAR1EDU X2PAR1EMP
/PRINT=TWOTAIL NOSIG
/STATISTICS DESCRIPTIVES XPROD
/MISSING=PAIRWISE.
Correlations
Notes
Output Created
11-JUL-2016 16:59:48
Comments
Input
Data
C:UsersucerDocuments183172_HS_Long_Study__student_.sav
Active Dataset
DataSet2
Filter
one>
Weight
one>
Split File
one>
N of Rows in Working Data File
23503
Missing Value Handling
Definition of Missing
User-defined missing values are treated as missing.
Cases Used
Statistics for each pair of variables are based on all the cases with valid data for that pair.
Syntax
CORRELATIONS
/VARIABLES=X2PAR1EDU X2PAR1EMP
/PRINT=TWOTAIL NOSIG
/STATISTICS DESCRIPTIVES XPROD
/MISSING=PAIRWISE.
Resources
Processor Time
00:00:00,06
Elapsed Time
00:00:00,09
[DataSet2] C:UsersucerDocuments183172_HS_Long_Study__student_.sav
Descriptive Statistics
Mean
Std. Deviation
N
T2 Parent 1: highest level of education
3,50
1,699
20919
T2 Parent 1: employment status
3,36
,895
20919
Correlations
T2 Parent 1: highest level of education
T2 Parent 1: employment status
T2 Parent 1: highest level of education
Pearson Correlation
1
,145**
Sig. (2-tailed)
,000
Sum of Squares and Cross-products
60375,708
4617,818
Covariance
2,886
,221
N
20919
20919
T2 Parent 1: employment status
Pearson Correlation
,145**
1
Sig. (2-tailed)
,000
Sum of Squares and Cross-products
4617,818
16758,009
Covariance
,221
,801
N
20919
20919
**. Correlation is significant at the 0.01 level (2-tailed).
The results covariance is 0.801 for the employment status revealing that there is a relationship between the parent's level of education and employment status. The social change in the improvement of parents' educational performances will improve the employment opportunities and their standard of living of their children. Moreover, the change will enhance the country economic growth because the parents will be more productive with their level of their education.
The study suggests that the policy makers should provide more incentives for parents to improve their educational qualifications to boost their employment status, which will assist them sending their children to the best schools in the United States.
Part 4
The study provides the critique of the research article titled "The Impact of Truancy on Educational Attainment during Compulsory Schooling: a Bivariate Ordered Probit Estimator with Mixed Effects." (Buscha, & Conte, 2014, p 103). The author uses the bivariate to investigate the relationship between truancy and educational attainment in schooling.
"One major factor in determining educational outcomes is truancy, which has been identified as a strong predictor of low educational attainment and 'poor life outcomes." (Buscha, & Conte, 2014, p 103). Thus, the bivariate and correlation analysis are the most appropriate choice because they assist the authors to estimate the distribution of effect of the truancy on educational attainment.
The authors display data using the frequency distributions revealing the level of truancy of students, higher educational attainment, frequencies, and weighted percent. Moreover, the data are displayed in a tabular form. The authors also display the results in a tabular form that assists in interpreting the results showing that the truancy affects the level of educational attainment.
Part 5
The multiple regression analysis assists in investigating whether the independent variables have effect of dependent variable. The research question is as follows:
Do school control, school locale and school geographical region influence science teachers self-efficacy?
The variables to answer the research question are as follows:
T1 School control
T1 School locale (urbanicity)
T1 School geographic region, and T1 Scale of science teacher's self-efficacy.
Thus, the multiple regressions intends to investigate whether independent variables (the school control, school locale and school geographical region) influence the dependent variable (science teacher self-efficacy).
The procedure is as follows:
In the SPSS Version 21, Start by Clicking:
Analyze ? Regression ?Linear
Put the variable science teacher self-efficacy in the dependent box
Put the Variables the school control, school locale and school geographical region in the Independent box
Click Statistics
Select Confidence Interval, Estimates, and Model Fit
Click Continue
Click OK.
The output of the Multiple Regression Statistics is as follows:
Regression
[DataSet1] C:UsersucerDownloadsHS Long Study_[student].sav
Variables Entered/Removeda
Model
Variables Entered
Variables Removed
Method
1
T1 School geographic region, T1 School locale (urbanicity), T1 School controlb.
Enter
a. Dependent Variable: T1 Scale of science teacher's self-efficacy
b. All requested variables entered.
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
,196a
,038
,038
,97086
a. Predictors: (Constant), T1 School geographic region, T1 School locale (urbanicity), T1 School control
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
515,155
3
171,718
182,183
,000b
Residual
12890,457
13676
,943
Total
13405,612
13679
a. Dependent Variable: T1 Scale of science teacher's self-efficacy
b. Predictors: (Constant), T1 School geographic region, T1 School locale (urbanicity), T1 School control
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
95,0% Confidence Interval for B
B
Std. Error
Beta
Lower Bound
Upper Bound
1
(Constant)
-,472
,045
-10,413
,000
-,561
-,383
T1 School control, 467
,022
,184
21,008
,000
,423
,510
T1 School locale (urbanicity)
-,028
,008
-,032
-3,639
,000
-,043
-,013
T1 School geographic region, 036
,009
,034
4,050
,000
,019
,054
a. Dependent Variable: T1 Scale of science teacher's self-efficacy
Based on the results of the multiple regression analysis, the school control, school locale and school geographical region do not influence the science teacher self-efficacy because the R Square is 0.38 which indicates low relationships between dependent and independent variables. The study suggests that the policy makers should focus on more effective variables to enhance science teachers efficacy.
Part 6
Verdejo-Garc?'a, Lo'pez-Torrecillas, Aguilar de Arcos, et al. (2005) use the multiple regression analysis to establish different effects of cannabis, cocaine, and MDMA on users. The multiple regression analysis is appropriate because drugs have been demonstrated to have multiple severity impacts on users. Moreover, multiple regression is appropriate in carrying out tests because it accommodates different variables. The authors display data of the participants that include socio-demographic characteristics, duration of use of drug, average dose, and frequencies. Part of the data used for the analysis is presented below:
Variable
Frequency
Percentage
Sex
Male
32
84.2
Female
6
15.8
Average
S.D.
Maximum
Minimum
Age
30.55
5.67
44
23
Years of schooling
10.05
2.35
15
6
Substance
Average Dose Frequency per month x (per episode)
Average
S.D.
Maximum
Minimum
Cannabis (fags)
0
Cocaine (g)
25.72
24.75
90
0
Heroin (g)
33.82
33.61
0
MDMA (pills)
6.47
17.17
96
0
Alcohol (units)
0
Substance
Duration of drug use (years)
Average
S.D.
Maximum
Minimum
Cannabis (fags)
8.10
6.10
20
0
Cocaine (g)
6.86
4.92
16
0
Heroin (g)
5.88
5.22
17
0
MDMA (pills)
0.80
1.68
7
0
Alcohol (units)
11.68
4.90
19
0
Abstinence (months)
4.76
4.90
19
0.50
Source: (Verdejo-Garc?'a, et al. 2005 p 89).
The authors present the results in the tabular forms making the results to stand alone.
The overall "results showed a differential impact of severity of MDMA abuse on working memory and abstract reasoning indices, of cocaine severity on an inhibitory control index and of cannabis on a cognitive flexibility index." (Verdejo-Garc?'a, et al. 2005 p 89).
Part 7
The research question to be answered with the dummy variables is as follows:
Do school locale and school geographical region influence the school control?
The study creates Dummy variables for:
T1 School control
T1 School locale (urbanicity)
T1 School geographic region, and A process to create Dummy variables is as follows:
Click
Transform ? Recode into Different Variables
Put T1 School control in Numeric Variable Output Variable box
Write Control in the Name
Write DummyControl in the Label
Click Change
Click Old and New Values
Put 1 in Old Value and 1 in New Value
Click Add
Click Continue
Click OK.
Use the same procedures for other variables to answer the research question. Thus, the study uses multiple regression by using the Dummy variable to answer the research questions:
Thus, the multiple regressions intends to investigate whether independent variables (school locale and school geographical region) influence the dependent variable ( the school control).
The procedure is as follows:
In the SPSS Version 21, Start by Clicking:
Analyze ? Regression ?Linear
Put the Dummy Variable Control in the dependent box
Put the Variables DummyControl, DummyLocale and DummyRegionschool in the Independent box
Click Statistics
Select Confidence Interval, Estimates, and Descriptive
Click Continue
Click OK.
The output of the Multiple Regression Statistics for the dummy variables is as follows:
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS CI (95)
/CRITERIA=PIN (.05) POUT (.10)
/ORIGIN
/DEPENDENT Control
/METHOD=ENTER Locale Region.
Regression
Notes
Output Created
11-JUL-2016 18:41:19
Comments
Input
Data
C:UsersucerDocuments183172_HS_Long_Study__student_.sav
Active Dataset
DataSet2
Filter
one>
Weight
one>
Split File
one>
N of Rows in Working Data File
23503
Missing Value Handling
Definition of Missing
User-defined missing values are treated as missing.
Cases Used
Statistics are based on cases with no missing values for any variable used.
Syntax
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS CI (95)
/CRITERIA=PIN (.05) POUT (.10)
/ORIGIN
/DEPENDENT Control
/METHOD=ENTER Locale Region.
Resources
Processor Time
00:00:00,09
Elapsed Time
00:00:00,15
Memory Required
5616 bytes
Additional Memory Required for Residual Plots
0 bytes [DataSet2] C:UsersucerDocuments183172_HS_Long_Study__student_.sav
Descriptive Statisticsa
Meanb
Root Mean Square
N
DummyControl
1,0000
1,00000
DummyLocale
1,0000
1,00000
DummyRegion
1,0000
1,00000
a. Coefficients have been calculated through the origin.
b. The observed mean is printed
Correlationsa
DummyControl
DummyLocale
DummyRegion
Std. Cross-product
DummyControl
1,000
1,000
1,000
DummyLocale
1,000
1,000
1,000
DummyRegion
1,000
1,000
1,000
Sig. (1-tailed)
DummyControl.
,000
,000
DummyLocale
,000.
,000
DummyRegion
,000
,000.
N
DummyControl
DummyLocale
DummyRegion
a. Coefficients have been calculated through the origin.
Variables Entered/Removeda, b
Model
Variables Entered
Variables Removed
Method
1
DummyRegionc.
Enter
a. Dependent Variable: DummyControl
b. Linear Regression through the Origin
c. Tolerance =, 000 limits reached.
Coefficientsa, b
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
95,0% Confidence Interval for B
B
Std. Error
Beta
Lower Bound
Upper Bound
1
DummyRegion
1,000
,000
1,000..
1,000
1,000
a. Dependent Variable: DummyControl
b. Linear Regression through the Origin
Excluded Variablesa, b
Model
Beta In t
Sig.
Partial Correlation
Collinearity Statistics
Tolerance
1
DummyLocale
.c.
,000
a. Dependent Variable: DummyControl
b. Linear Regression through the Origin
c. Predictors in the Model: DummyRegion
The screen shot of the dummy variables is revealed below:
Part 8
The Chi square assists in investigating whether the categorical variables differs from one another. This section use the chi-square to answer the following question.
Is the parent's level of education influence the employment status?
The study collects data from the "High School Longitudinal Study dataset" and answer research question using Chi square.
The variables used for the analysis is as follows:
T2 Parent 1: highest level of education
T2 Parent 1: employment status
The procedure is as follows:
In the SPSS Version 21, Start by Clicking:
Analyze ? Descriptive Statistics ?Crosstabs
Put the highest level of education in the Row box
Put the employment status in the Column box
Click Statistics
Select Chi Square
Select Phi and Cramer's V
Click Continue
Click OK.
The output of the Chi Square is as follows:
CROSSTABS
/TABLES=X2PAR1EDU BY X2PAR1EMP
/FORMAT=AVALUE TABLES
/STATISTICS=CHISQ PHI
/CELLS=COUNT
/COUNT ROUND CELL.
Crosstabs
Notes
Output Created
12-JUL-2016 07:44:43
Comments
Input
Data
C:UsersucerDocuments183172_HS_Long_Study__student_.sav
Active Dataset
DataSet2
Filter
one>
Weight
one>
Split File
one>
N of Rows in Working Data File
23503
Missing Value Handling
Definition of Missing
User-defined missing values are treated as missing.
Cases Used
Statistics for each table are based on all the cases with valid data in the specified range(s) for all variables in each table.
Syntax
CROSSTABS
/TABLES=X2PAR1EDU BY X2PAR1EMP
/FORMAT=AVALUE TABLES
/STATISTICS=CHISQ PHI
/CELLS=COUNT
/COUNT ROUND CELL.
Resources
Processor Time
00:00:00,05
Elapsed Time
00:00:00,04
Dimensions Requested
2
Cells Available
131029
[DataSet2] C:UsersucerDocuments183172_HS_Long_Study__student_.sav
Case Processing Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
T2 Parent 1: highest level of education * T2 Parent 1: employment status
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