Presentation

This is to present the output from SPSS into a different software

For this, I selected the histogram to be exported to Microsoft Word

The steps are;

The result in Microsoft Word;

Normal Distribution of Data (Numerical)

The second method to check the normality of the data is through a numerical method

I am going to use the kurtosis and skewness

The steps that I took are;

The result and interpretation;

For skewness's value: I divide -0.625 with 0.464 where I would get -1.3469

• -1.3469 is smaller than 1.96 indicates that the data is normal

For kurtosis's value: I divide 0.018 with 0.902 where I would get 0.0199

• 0.0199 is also smaller than 1.96 indicates that the data is normal

Normal Distribution of Data (Graphical)

There are few ways to check the normality of data before we could do a hypothesis test on the data collected

Firstly, I am going to check the data based on graphical method; Histogram

The steps taken are;

The result and interpretation;

For the variable AverageSA, the normal curve on the histogram shows that the data is slightly negatively skewed with Mean = 91.12 and SD = 16.378.

Recode

For recoding, I am going to reverse code one of my variables

As I have highlighted in the figure, I am going to reverse code the data for the variable LNTB1

The steps taken to recode are;

And the result of recoding is;

Note: For the result figure, I have changed the variable name from the Variable View because I mistakenly missed out the number '1' from the original variable name LNTB1

Data Screening & Removal of Outliers

DATA SCREENING

For the data screening, I am going to screen the nominal and ordinal variables

What I got in the output window are;

1. Overall statistics of demographics

2. Frequency of ID

3. Frequency of Age and Gender

4. Frequency of Month of Birth and Year of Birth

REMOVAL OF OUTLIERS

Based on this screening, I found that;

1. There is one extreme value for the variable Age

There is less likely for a 210 year- old to fill up these scales, therefore this participant is considered as outlier.  I am going to remove the outlier; remove the participant from the respondents

As it is easy to locate the outlier, I would not do the cases sorting

After deleting the case for the outlier, I then do a data screening again for the variable Age

Transform

For this element, I am going to compute a new variable.

Based on the 30 items that measure social phobia/ anxiety, I will compute the average score for each respondent.

Target Variable: Average score for Social Anxiety (AverageSA)
Numeric Expression: Summation of the score of the 30 items divided by 30

Computing the variables

This is the result of the variable computation;

Naming of Variables and Their Characteristics

My variables include;

1. ID
2. Age
3. Gender
4. Month of Birth
5. Year of Birth
6. 30 items for my first scale
7. 10 items for my second scale

Demographics

For the first scale, each item is answered on a 5- point Likert scale where the higher the score in any dimensions, the more anxiety the person has in that dimension

I specified its variables' names because the items measure the social phobia/ anxiety based on five factors. Each factor has six items.

The five factors are;

1. Speaking in public/ talking with people in authority (I specified as PA)
2. Interactions with the opposite sex (I specified as OS)
3. Assertive expression of annoyance, disgust, or displeasure (I specified as AE)
4. Criticism and embarrassment (I specified as CE)
5. Interactions with strangers (I specified as IS)

First scale items

First scale items (cont..)

For the second scale, I classified the items based on two aspects;

1. High in the need to belong
2. Low in the need to belong

and I named the items as;

1. High in the need to belong as HNTB (measure by seven items)
2. Low in need to belong as LNTB (measured by three items)

Based on this classification;

• If a person scores high in the items for HNTB, the person has higher need to belong
• If a person scores high in the items for LNTB, the person has lower need to belong
• If a person scores low in the items for LNTB, the person has higher need to belong
• If a person scores low in the items for HNTB, the person has lower need to belong

Second scale items

Data Entry

This is the evidence of source of the data
file:///C:/Users/user/AppData/Local/Temp/Rar$EX83.032/Form%20responses%201.html

I entered all the data in the Data View and I do few editing in the Variable View

My Scales and Responses

Both scales I chose were from Measurement Instrument Database for the Social Sciences (MIDSS)
http://www.midss.org/
I chose these scales on the 19th of September 2014

My first scale is "Social Anxiety Questionnaire for Adults" by Caballo, V. E., Salazar, I. C., Irurtia, M. J., Arias, B., and CISO-A Research Team,
http://www.midss.org/content/social-anxiety-questionnaire-adults-saq-a30

My second scale is "Need to Belong Scale" by Leary, M.R,
http://www.midss.org/content/need-belong-scale

This is the combined scales
https://docs.google.com/forms/d/1H2cbybE-YCvP3d3gv0EioDy1cwxcf_5svHal75xOC4Y/viewform

On 27th September 2014, I gave out my scales to a group in Facebook which consists of 175 members.

As of 28th September 2014 at 9:20 am, I have 22 responses and by 1st October 2014 at 3:37 pm, I have 29 responses.

I then delete the post in the group so that I would not receive anymore responses.