Assignment: Interpreting Your Results – nursing homework essays

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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

1. 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.

2. 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

3. 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