Assignment: Interpreting Your Results

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Manipulating the Dataset in SPSS
Layal Mansour
Walden University
 
 
 
 
 
 
 
 
 
 
 
 
Manipulating the Dataset in SPSS
Manipulating data involve the process of converting available data into a more organized and easy to understand in a bid to make informed decisions. As a result, the end-results from a data manipulation is displaying of information to the users, in a meaningful and usable way.
Research Questions
The research questions in the study are

Is the number of bedrooms per housing unit dependent on the size of land a house occupies?
Is there a relationship between the fuel used in heating the house and the number of bedrooms per housing unit?

Null Hypothesis

The number of bedrooms per housing unit is not dependent on the size of the land the house occupies.
There is no association between the fuel used in heating a house and the number of bedrooms per a housing unit.

Variables Definition and Categories
Bedrooms in a housing unit, lot size, and house heating fuel are the variable that will answer the defined research questions. The variable bedrooms contain a number of categories includedno bedroom, one bedroom, two bedrooms, three bedrooms, and fourbedrooms, five or more bedrooms. Similarly, the element lot size has a number of categories, which include a house on less than one-acre land, house on one to less than 10 acres of land as well as a house on ten or more acres of land.House heating fuel as well contain elements such as utility gas, bottled, tank, or LP Gas, Electricity, Fuel oil, kerosene, etc., Coal or coke, wood, solar energy, other fuel or no fuel used in heating.
Data Conversion from Continuous to Categorical
The selected elements for the analysis are categorical in nature, revealing a nominal level of measurement. Therefore, none of the elements requires converting from continuous to a categorical variable.
Exploration of the Categorical Variables Results
The data exploration entails both the descriptive and analytical analysis in SPSS and presentation of the findings in standalone tables for easier interpretation of the results.
Descriptive Statistics

Lot size

The table below illustrates the frequencies for the variable Lot size involving the number of acres.

Table 1
Lot size

 
Frequency
Percent
Valid Percent
Cumulative Percent

Valid
N/A (not a one-family house or mobile home)
2207
15.3
15.3
15.3

House on less than one acre
11298
78.4
78.4
93.7

House on one to less than ten acres
770
5.3
5.3
99.1

House on ten or more acres
132
.9
.9
100.0

Total
14407
100.0
100.0
 

 
Inference
The frequency table 1 above reveals various the number and percentage for each categoryin the variable lot size. From frequency table, 15 % of the sampled houses were mobile home/ not a one family house, while 78.4% entailed houses built on less than one acre of land.Correspondingly, houses on one to less than ten acres and house on ten or more acres depicts 5.3% and 0.9% respectively.

Bedrooms

The table below reveals the frequencies for the variable bedroom, indicating the number of bedrooms per unit house.

Table 2
Bedrooms

 
Frequency
Percent
Valid Percent
Cumulative Percent

Valid
No bedrooms
392
2.7
2.7
2.7

1 Bedroom
884
6.1
6.1
8.9

2 Bedrooms
2972
20.6
20.6
29.5

3 Bedrooms
7676
53.3
53.3
82.8

4 Bedrooms
2103
14.6
14.6
97.4

5 or more Bedrooms
380
2.6
2.6
100.0

Total
14407
100.0
100.0
 

 
Inference
Table 2 reveals the frequencies for the number of bedrooms per a unit house based on the house record dataset collected from Puerto Rico. From the table, 3 bedroomed houses revealed the highest percent of the sample houses at 53.3%, followed closely by 2 bedroom houses at 20.6 percent of the total houses in the house record dataset. Subsequently, houses with no bedrooms depicted 2.7 percent, while 5 or more bedrooms house revealed 2.6 % of the collected house record data in Puerto Rico/ PR. Nonetheless, 1 and 4 bedroomed houses in the dataset posited 6.1% and 14.6 % respectively from the total house record data in the dataset. Assignment: Interpreting Your Results

House heating fuel

The table below illustrates the frequencies for the variable house heating fuel indicating various forms of fuel energy used in heating the houses based on the sampled house record data from Puerto Rico/PR.

Table 3
House heating fuel

 
Frequency
Percent
Valid Percent
Cumulative Percent

Valid
N/A (Vacant)
1547
10.7
10.7
10.7

Utility gas
22
.2
.2
10.9

“Bottled, tank, or LP gas”
357
2.5
2.5
13.4

Electricity
2457
17.1
17.1
30.4

“Fuel oil, kerosene, etc.”
7
.0
.0
30.5

Coal or coke
1
.0
.0
30.5

Wood
1
.0
.0
30.5

Solar energy
115
.8
.8
31.3

Other fuel
25
.2
.2
31.5

No fuel used
9875
68.5
68.5
100.0

Total
14407
100.0
100.0
 

 
Inference
From the frequency table 3 above, the largest number of houses did not use any form of fuel in heating the house at 68.5 % of the sample houses in Puerto Rico/ PR house record dataset. Nonetheless, 17.1 percent of the houses used electricity to heat the house, with 2.5 and 0.2% of the home using Bottled, tank, or LP gas and Utility gas respectively and the house heating fuel. Moreover, 0.8% and 0.2 of the houses used solar energy and other fuel respectively as house heating fuel while 10.7% of the houses in the database were vacant.
Analytical Statistics
The analytical process involved chi-square tests for independence aimed at answering the research questions. The table below illustrates the chi-square test between the number of bedrooms per house and the size of the land the house occupies.
 

Table 4
Chi-Square Tests

 
Value
df
Asymp. Sig. (2-sided)

Pearson Chi-Square
1725.580a
15
.000

Likelihood Ratio
1446.661
15
.000

Linear-by-Linear Association
749.667
1
.000

N of Valid Cases
14407
 
 

a. 2 cells (8.3%) have expected count less than 5. The minimum expected count is 3.48.

 
Inference
From table 4 above, the Pearson Chi-square value is 1725.580 at 15 degrees of freedom. Besides, the p-value is 0.000 less than α=0.05, implying that we reject the null hypothesis. Therefore, we are 95 % confident that the number of bedrooms per housing unit dependents on the size of the land the house occupies.
Moreover, the table below illustrates the chi-square test between the fuel used in heating the house and the number of bedrooms per housing unit.

Table 5
Chi-Square Tests

 
Value
df
Asymp. Sig. (2-sided)

Pearson Chi-Square
381.670a
45
.000

Likelihood Ratio
354.759
45
.000

Linear-by-Linear Association
175.228
1
.000

N of Valid Cases
14407
 
 

a. 29 cells (48.3%) have expected count less than 5. The minimum expected count is .03.

 
Inference
From the chi-square table 5 above, the Pearson Chi-square value is 381.670 with 45 degrees of freedom.The p-value from the table is 0.000 less than α= 0.05 depicting that we reject the null hypothesis.Thus, we are 95 percent confident that the fuel used in heating the house is associated with the number of bedrooms in a house. Assignment: Interpreting Your Results