Exploratory Data Analysis in SPSS Chapter

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SPSS

How did you treat missing or oddly coded data, and outliers?

The SPSS function for Missing Data was used to identify any outliers or oddly coded data. The percent of missing data is high at 47.2% or 1063 potential responses to the question. The number of extremes or outliers in the high range is 220. Because the numbers are high, it would be useful to look at the raw data to determine how these answers were coded. This question is an interesting one and the pattern of responses suggests that additional analysis with this variable could reveal relationships with other factors.

What did you visually observe about your variables?

Responses to some of the questions are clustered for a number of the survey respondents. That is to say that there appear to be some respondents in the sample who are very active users of their cell phones and other respondents who barely use their cell phones.

What were the results of testing for normality for scale (interval or ratio) variables?

The Kolmogorov-Smirnov test for normality was used because of the size of N, although both Shapiro-Wilk and Kolmogorov-Smirnov statistics are shown in the data table. The p-value is

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Estimating skewness by establishing a range of twice the standard error (.071 x 2 = 0.142) in a negative and positive direction demonstrates that the data is skewed. Similarly, establishing a range of normality to estimate the kurotsis by multiplying the standard error of Kurtosis by 2 (.142 x 2 = 02.84) and showing minus that value to plus that value.

How would you state a potential null and alternative hypotheses that might be of interest and important to the test, based on your discoveries in this data?

Ho = The number of text messages received on average each day is not related to the number of moving vehicle tickets received by drivers in the study.

Ha = The number of text messages received on average each day is related to the number of moving vehicle tickets received by drivers in the study.

DESCRIPTIVES VARIABLES=q20

/STATISTICS=MEAN SUM STDDEV VARIANCE RANGE MIN MAX SEMEAN KURTOSIS SKEWNESS.

Descriptives

Notes

Output Created

07-AUG-2015 10:17:00

Comments

Input

Data

/Users/Downloads/171243_May_2010_Cell_Phones.sav

Active Dataset

DataSet1

Filter

one>

Weight

one>

Split File

one>

N of Rows in Working Data File

Missing Value Handling

Definition of Missing

User defined missing values are treated as missing.

Cases Used

All non-missing data are used.

Syntax

DESCRIPTIVES VARIABLES=q20

/STATISTICS=MEAN SUM STDDEV VARIANCE RANGE MIN MAX SEMEAN KURTOSIS SKEWNESS.

Resources

Processor Time

00:00:00.02

Elapsed Time

00:00:00.00

[DataSet1] / Users/Downloads/171243_May_2010_Cell_Phones.sav

Descriptive Statistics

N

Range

Minimum

Maximum

Sum

Mean

Std. Deviation

Statistic

Statistic

Statistic

Statistic

Statistic

Statistic

Std. Error

Statistic

Q20. On an average day, about how many text messages do you send and receive on your cell phone?

0

72991

61.39

5.330

Valid N (listwise)

Descriptive Statistics

Variance

Skewness

Kurtosis

Statistic

Statistic

Std. Error

Statistic

Std. Error

Q20. On an average day, about how many text messages do you send and receive on your cell phone?

33773.093

4.280

.071

18.173

.142

Valid N (listwise)

DESCRIPTIVES VARIABLES=q20

/STATISTICS=MEAN SUM STDDEV VARIANCE RANGE MIN MAX SEMEAN KURTOSIS SKEWNESS.

Descriptives

Notes

Output Created

07-AUG-2015 10:17:00

Comments

Input

Data

/Users/Downloads/171243_May_2010_Cell_Phones.sav

Active Dataset

DataSet1

Filter

one>

Weight

one>

Split File

one>

N of Rows in Working Data File

Missing Value Handling

Definition of Missing

User.....

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