## Quant: Group Statistics in SPSS

Introduction

The aim of this analysis is to make a decision about whether a person is alive or dead ten years after a coronary is reflected in a significant difference in his diastolic blood pressure taken when that event occurred. The variable “DBP58” will be used as a dependent variable and “Vital10” as an independent variable.

From the SPSS outputs the goal is to:

• Analyze these conditions to determine if there is a significant difference between the DBP levels of those (vital10) who are alive 10 years later compared to those who died within 10 years.

Hypothesis

• Null: There is no basis of difference between the DBP58 and Vital10
• Alternative: There is are real differences between the DBP58 and Vital10

Methodology

For this project, the electric.sav file is loaded into SPSS (Electric, n.d.).  The goal is to look at the relationships between the following variables: DBP58 (Average Diastolic Blood Pressure) and Vital10 (Status at Ten Years). To conduct a parametric analysis, navigate to Analyze > Compare Means > Paired-Samples T Test.  The variable DBP58 was placed in the “Test Variables” box, and Vital10 was placed under “grouping variable” box.  Then select the “Define Groups” button and enter 0 for “Group 1” and 1 for “Group 2”.  The procedures for this analysis are provided in video tutorial form by Miller (n.d.). The following output was observed in the next two tables.

Results

Table 1: Group Statistics

 Status at Ten Years N Mean Std. Deviation Std. Error Mean Average Diast Blood Pressure 58 Alive 178 87.56 11.446 .858 Dead 61 92.38 16.477 2.110

According to the results in Table 1, the mean diastolic blood pressure of those who have passed away ten years later was 5 points higher and had a huge standard deviation.  Thus, those who are alive ten years later have a smaller variation of their diastolic blood pressure.

Table 2: 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 Average Diast Blood Pressure 58 Equal variances assumed 8.815 .003 -2.515 237 .013 -4.815 1.915 -8.587 -1.043 Equal variances not assumed -2.114 80.735 .038 -4.815 2.277 -9.347 -.284

According to the independent t-test for equality of means, shows that there is no equality in the variance at the 0.05 level, such that when equal variances are not assumed, the null hypothesis could be rejected at the 0.05 level because the significance value is 0.038.  Thus, there is a statistically significant difference between the means of diastolic blood pressure of those who are alive and those who have passed away.

SPSS Code

DATASET NAME DataSet1 WINDOW=FRONT.

T-TEST GROUPS=vital10(0 1)

/MISSING=ANALYSIS

/VARIABLES=dbp58

/CRITERIA=CI(.95).

References:

## Quant: Paired Sample Statistics in SPSS

Introduction

The aim of this analysis is to conduct a comparison of productivity under two organizational structures: The data are artificial estimates of productivity with column 1 representing traditional vertical management and column 2 representing other autonomous work teams (ATW). The background is that a company of 100 factory workers had been operating under traditional vertical management and decided to move to ATW. The same employees were involved in both systems having first worked under vertical management and then being converted to ATW.

From the SPSS outputs the goal is to:

• Analyze the productivity levels of the 2 management approaches, and decide which is superior.

Hypothesis

• Null: There is no basis of difference between the prodpre and prodpost
• Alternative: There is are real differences between the prodpre and prodpost

Methodology

For this project, the atw.sav file is loaded into SPSS (ATW, n.d.).  The goal is to look at the relationships between the following variables: prodpre (productivity level preceding the new process) and prodpost (productivity level following the new process). To conduct a parametric analysis, navigate to Analyze > Compare Means > Paired-Samples T Test.  The variable prodpre was placed in the “Paired Variables” box under “Pair” 1 and “Variable 1”, and prodpost was placed under “Pair” 1 and “Variable 2”.  The procedures for this analysis are provided in video tutorial form by Miller (n.d.). The following output was observed in the next three tables.

Results

Table 1: Paired Sample Statistics

 Mean N Std. Deviation Std. Error Mean Pair 1 productivity level preceding the new process 76.43 100 16.820 1.682 productivity level following the new process 84.24 100 9.797 .980

Descriptively, productivity on average increased by 8 points, and the standard deviation about the mean decreased by 7 points.  This means that the estimates of productivity under the traditional vertical management are less than and showcase a wider spread than those of the estimates of productivity under the autonomous work teams.  Essentially these distributions tell the story that the workers are getting better productivity estimates with less deviation under autonomous work teams.

Table 2: Paired Samples Correlation

 N Correlation Sig. Pair 1 productivity level preceding the new process & productivity level following the new process 100 .040 .695

Based on Table 2, there is a weak correlation (r = 0.040) between the estimates of productivity under the traditional vertical management and the autonomous work teams.  Although correlation does not imply causation.

Table 3: 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 productivity level preceding the new process – productivity level following the new process -7.817 19.126 1.913 -11.612 -4.022 -4.087 99 .000

Based on the results from the 2-tailed student t-tests (Table 3), the null hypothesis can be rejected.  There is a significant difference between the two variables prodpre and prodpost at the 0.05 level or less.  The data based on 100 workers (with degrees of freedom of 99) show that there is a significance in the estimates of productivity under the traditional vertical management and the autonomous work teams.

SPSS Code

DATASET NAME DataSet1 WINDOW=FRONT.

T-TEST PAIRS=prodpre WITH prodpost (PAIRED)

/CRITERIA=CI(.9500)

/MISSING=ANALYSIS.

References: