Interpret statistical output

 

Instructions

Now that you have analyzed your data, you will need to interpret the output that you obtained from your data analysis. Specifically, you need to discuss what the data analysis findings mean in relation to your research questions and hypotheses, and what actions should be taken as a result. 

For this assignment, you must provide a narrative that discusses the key insights from your data analysis findings and highlights the limitations of your analysis.  Limitations should pertain to weaknesses in your design and limits on your ability to make conclusions.  For example, if you are not able to determine cause and effect, that would be a limitation. If your dataset is small, that would be another limitation. 

Length: 5-7 pages, not including title and reference pages

References: Include a minimum of 5 scholarly resources.

The completed assignment should demonstrate thoughtful consideration of the ideas and concepts presented in the course by providing new thoughts and insights relating directly to this topic. The content should reflect scholarly writing and current APA standards and should adhere to Northcentral University’s Academic Integrity Policy.

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t

udent name:

Institution name:

Tittle: A statistical analysis of video games

Problem description

This report details the outcomes of the data analysis of a video game data set. The analysis involved carrying out various hypothesis tests to ascertain the validity of particular claims about the data. The data analysis was carried out using the statistical software for social sciences (SPSS). The statistical tests conducted were Student’s t-test for the difference in means between groups along with descriptive and visual analysis of the data (Kim,

2

0

1

5). T-tests were used because each of the grouping variables (independent variables) consisted of only two categories (Rhemtulla, Brosseau-Liard, & Savalei, 2012). The specific questions to be addressed were whether the number of video games visits varied with the type of video game on show and whether the amount of visit time in a video game are different for the type of video game on show. Additionally, questions of whether the number of video game visits and the time taken for each visit varied with whether or not an advertisement of the video game was carried out.

Hypotheses were formulated and tested to help answer the above research questions. Given that each independent/grouping variable was associated with two research questions, a total of

4

hypotheses were formulated. The hypotheses were as below;

Video game type and number of video game visits

H0: There is no difference in the mean number of video game visits for the two types of video games (police or thief).

H1: There is a difference in the mean number of video game visits for the two types of video games (police or thief).

Video game type and the amount of visiting time

H0: There is no difference in the mean amount of visiting time for the two types of video games on offer (police or thief).

H1: There is a difference in the mean amount of visiting time for the two types of video games on offer (police or thief).

Advertising

and the number of video games visits

H0: Advertising has no influence on the number of times a video game is visited/ the mean number of times a video game is visited is the same with or without advertisement.

H1: Advertising has an influence on the number of times a video game is visited/ the mean number of times a video game is visited varies with whether or not an advertisement is carried out.

Advertising and the amount of visiting time for a video game

H0: The amount of visiting time for a video game does not vary with whether or not an advertisement is carried out for the video game/ the mean amount of visiting time for a video game is the same with or without advertisement.

H1: The amount of visiting time for a video game does vary with whether or not an advertisement is carried out for the video game/ the mean amount of visiting time for a video game is dependent on whether or not an advertisement is carried out for the video game.

All the above hypotheses were tested at the 0.05 level of significance (Wasserstein & Lazar, 2016). This implies that the null hypothesis is rejected if the obtained p-value is less than 0.05 while we fail to reject it if the obtained p-value is greater than 0.05 (Anderson, Burnham, & Thompson, 2000).

The data

As already mentioned, the data used for this analysis is a video game data recording various aspects of the games such as the type of game, whether or not an advertisement was carried out, the number of times a video game was visited, the amount of visiting time for each visit, the total amount of time for each visit on a particular day and the day the visit occurred. The data has a total of 6 variables each with

44

observations. Two of the variables of interest were re-coded in SPSS to transform them from type string to type numeric to allow for the appropriate data analysis procedure (Escalera, Pujol, & Radeva, 20

10

). These variables were

Game

and Advertising which needed to be converted to numeric categorical/grouping variables. Of the six variables, two, that is

Date

and Totaltime, were not used in the analysis. The table below illustrate each of the variable in the data;

Variable

Type

Description

Re-coded as

Re-coded as type

Date

String

The day of the week on which the video game visit took place.

Visits

N

umeric

The number of video game visits on a particular day of the week

VisitTime

Numeric

How long each video game visit lasted.

TotalTime

Numeric

The total amount of time a video game was visited on a particular day of the week

Game

String

A string variable denoting the type of game on show (police or thief)

Gamerecoded

Values of 2 and

3

were used to denote whether the game was police or thief respectively.

Numeric
Advertising

String

A string variable denoting whether an advertisement was carried out for the video game or not (yes for advertisement and no for no advertisement).

Advertisingrecoded

Values 1 and 4 were used to denote whether or not an advertisement was carried out for the video game (1 for no and 4 for yes).

Numeric

Descriptive statistics and visualizations

Before data analysis was carried out to answer the research questions, descriptive statistics as well as visualization of the variables in the data were carried out to give an idea of the distribution as well as the central tendencies of the variables. The table below illustrates the results of the descriptive analysis;

Visits

10

VisitTime

44

4.44

TotalTime

44

.00

28.45

Gamerecoded

44

Advertisingrecoded

44

3

1

2.50

44

Descriptive Statistics
			N	Range	Minimum	Maximum				Mean				Std. Deviation
					44		10	0	1.45	2.672
	4.44		.00	.7050	1.09071
	28.45	2.8702	6.00944
	1	2		3		2.50	.506
4	1.517
Valid N (listwise)

As can be seen from the table, the highest number of visits in a particular day was 10 while the least was 0. The mean number of visits was 1.45 with a standard deviation of 2.672. The longest visit lasted a maximum of 4.44 hours with some visits lasting as little as 0.00 hours. The mean amount of visiting time was 0.705 hours with a standard deviation of 1.091. The highest total amount of time a video game was visited in a particular day was 28.45 hours. The mean total visit amount was 2.8702 with a standard deviation of 6.0094. The type of video game was either police (2) or thief (3) while a video game was either advertised (1) or not (4). All the observations in each of the variables were valid.

To assess the distribution of the variables, histograms of the important ones (visits, visittime) were plotted. The histogram for each variable is as displayed below;

The number of visits follow no particular distribution with the range 0-1 capturing the most data points. This implies that the video games received no visitors on a frequent basis.

Most video game visits lasted between 0 and 2 hours on the maximum with a good number of the remaining visits lasting between 2-3 hours.

T-tests, results and discussion

Independent sample t-test for the difference in means were carried out to test the hypotheses formulated in the first section above. The results of each of the tests were as displayed in the below tables

Gamerecoded

N

Mean

Std. Deviation

VisitTime

22

		Group Statistics
		Std. Error Mean
	Police								22	.5250	.90210	.19233
	Theif	.8850	1.24671	.26580

VisitTime

-1.097

.279

-.36000

.32808

		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
			Equal variances assumed	1.955	.169		-1.097				42		.279		-.36000		.32808	-1.02210	.30210
			Equal variances not assumed	38.259	-1.02402	.30402

Dependent variale; visittime: independent variable; Gamerecoded

The 2-tailed p-value (significance) assuming unequal variances is 0.279 which is higher than the 0.05 level of significance. We therefore fail to reject the null hypothesis and conclude that there is no difference in the mean amount of visiting time for the two types of video games on offer (police or thief).

The t-test results for the number of visits to the video games and type of game are as displayed in the tables below;

Group Statistics

Gamerecoded

N

Mean

Std. Deviation

Std. Error Mean

Visits

Police

22

Theif

22

1.41	2.594	.553
1.50	2.807	.599

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

Visits

Equal variances assumed

42

Equal variances not assumed

-.112

.912

-.091

.815

-1.736

1.554

.025	.875		-.112		.912		-.091		.815		-1.736		1.554
41.741

Dependent variable; Visits: Independent variable; Gamerecoded

From the test results above, the p-value is once again 0.912>0.05. Again, we fail to reject the null hypothesis and conclude that there is no difference in the mean number of video game visits for the two types of video games (police or thief).

The t-test results for the video game visits as well as visiting time are as displayed in the below tables;

Group Statistics

Advertisingrecoded

N

Mean

Std. Deviation

Std. Error Mean

VisitTime

22

22

Visits

No

22

Yes

22

	No	.2441	.69344	.14784
	Yes	1.1659	1.22881	.26198
.23	.685	.146
2.68	3.315	.707

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

VisitTime

Equal variances assumed

42

Equal variances not assumed

-3.064

.004

-.92182

.30082

Visits

Equal variances assumed

42

Equal variances not assumed

-3.401

-2.455

.722

8.405	.006		-3.064		.004		-.92182		.30082	-1.52890	-.31474
33.144	-1.53374	-.30990
50.261	.000		-3.401	.001		-2.455		.722	-3.911	-.998
22.792	.002	-3.948	-.961

Dependent variables; Visittime and visit: Independent variable; Advertisementrecoded

From the analysis results above, the p-value for the visit time is 0.004 <0.05. The null hypothesis is therefore, rejected and the conclusion that the amount of visiting time for a video game does vary with whether or not an advertisement is carried out for the video game is drawn. On the other hand the p-value for the number of visits is 0.002<0.05. The null hypothesis is again rejected and the conclusion that advertising has an influence on the number of times a video game is visited is drawn.

References
Anderson, D. R., Burnham, K. P., & Thompson, W. L. (2000). Null hypothesis testing: problems, prevalence, and an alternative. The journal of wildlife management, 912-923.
Escalera, S., Pujol, O., & Radeva, P. (2010). Re-coding ECOCs without re-training. Pattern Recognition Letters. Pattern Recognition Letter.
Kim, T. K. (2015). T test as a parametric statistic. Korean journal of anesthesiology, 540.
Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological methods, 354.
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: context, process, and purpose. The American Statistician, 129-133.

outputViewer0000000000.xml
Output Log

GET DATA  /TYPE=TXT
  /FILE=”D:\Downloads\excelforgames.txt”
  /DELCASE=LINE
  /DELIMITERS=”\t”
  /ARRANGEMENT=DELIMITED
  /FIRSTCASE=2
  /IMPORTCASE=ALL
  /VARIABLES=
  Date A9
  Visits F2.0
  VisitTime F4.2
  TotalTime F5.2
  Game A6
  Advertising A3.
CACHE.
EXECUTE.
DATASET NAME DataSet1 WINDOW=FRONT.
AUTORECODE VARIABLES=Game Advertising
  /INTO Gamerecoded Advertisingrecoded
  /GROUP
  /BLANK=MISSING
  /PRINT.
User-missing values from Advertising
Old Value  New Value  Value Label
No                 1  No
Police             2  Police
Theif              3  Theif
Yes                4  Yes

T-TEST GROUPS=Gamerecoded(2 3)
  /MISSING=ANALYSIS
  /VARIABLES=Visits
  /CRITERIA=CI(.95).

00000000011_lightNotesData.bin

00000000013_lightTableData.bin

00000000014_lightTableData.bin

outputViewer0000000001_heading.xml
Output T-Test Title

T-Test Notes 00000000011_lightNotesData.bin Active Dataset

[DataSet1]  Group Statistics 00000000013_lightTableData.bin Independent Samples Test 00000000014_lightTableData.bin

outputViewer0000000002.xml
Output Log

T-TEST GROUPS=Gamerecoded(2 3)
  /MISSING=ANALYSIS
  /VARIABLES=VisitTime
  /CRITERIA=CI(.95).

00000000031_lightNotesData.bin

00000000032_lightTableData.bin

00000000033_lightTableData.bin

outputViewer0000000003_heading.xml
Output T-Test Title

T-Test Notes 00000000031_lightNotesData.bin Group Statistics 00000000032_lightTableData.bin Independent Samples Test 00000000033_lightTableData.bin

outputViewer0000000004.xml
Output Log

T-TEST GROUPS=Advertisingrecoded(1 2)
  /MISSING=ANALYSIS
  /VARIABLES=VisitTime Visits
  /CRITERIA=CI(.95).

00000000051_lightNotesData.bin

00000000052_lightWarningData.bin

00000000053_lightTableData.bin

outputViewer0000000005_heading.xml
Output T-Test Title

T-Test Notes 00000000051_lightNotesData.bin Warnings 00000000052_lightWarningData.bin Group Statistics 00000000053_lightTableData.bin

outputViewer0000000006.xml
Output Log

T-TEST GROUPS=Advertisingrecoded(1 4)
  /MISSING=ANALYSIS
  /VARIABLES=VisitTime Visits
  /CRITERIA=CI(.95).

00000000071_lightNotesData.bin

00000000072_lightTableData.bin

00000000073_lightTableData.bin

outputViewer0000000007_heading.xml
Output T-Test Title

T-Test Notes 00000000071_lightNotesData.bin Group Statistics 00000000072_lightTableData.bin Independent Samples Test 00000000073_lightTableData.bin

outputViewer0000000008.xml
Output Log

DESCRIPTIVES VARIABLES=Visits VisitTime TotalTime Gamerecoded Advertisingrecoded
  /STATISTICS=MEAN STDDEV RANGE MIN MAX.

00000000091_lightNotesData.bin

00000000092_lightTableData.bin

outputViewer0000000009_heading.xml
Output Descriptives Title

Descriptives Notes 00000000091_lightNotesData.bin Descriptive Statistics 00000000092_lightTableData.bin

outputViewer0000000010.xml
Output Log

FREQUENCIES VARIABLES=Visits VisitTime TotalTime
  /FORMAT=NOTABLE
  /HISTOGRAM
  /ORDER=ANALYSIS.

00000000111_lightNotesData.bin

00000000112_lightTableData.bin

000000001131__chartData.bin

000000001131__chart.xml

Visits

Frequency

Visits

Mean =

Std. Dev. =

N =

000000001132__chartData.bin

000000001132__chart.xml

VisitTime

Frequency

VisitTime

Mean =

Std. Dev. =

N =

000000001133__chartData.bin

000000001133__chart.xml

TotalTime

Frequency

TotalTime

Mean =

Std. Dev. =

N =

outputViewer0000000011_heading.xml
Output Frequencies Title

Frequencies Notes 00000000111_lightNotesData.bin Statistics 00000000112_lightTableData.bin Histogram Title

Histogram Visits 000000001131__chartData.bin 000000001131__chart.xml VisitTime 000000001132__chartData.bin 000000001132__chart.xml TotalTime 000000001133__chartData.bin 000000001133__chart.xml

outputViewer0000000012.xml
Output Log

GET
  FILE=’C:\Users\13473\Desktop\7030 Assignments\Assgn 4\Assign Done\Videodata.sav’.
DATASET NAME DataSet1 WINDOW=FRONT.

META-INF/MANIFEST.MF
allowPivoting=true