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- REFERENCES

- Pallant, J. (2016) SPSS Survival Manual?: A Step by Step Guide to Data Analysis Using SPSS Program. 6th Edition, McGraw-Hill Education, London.

REPORT RESULT in APA

Descriptive

According to the table, the mean of religious score (M = 32.97, SD = 6.52). On the other hand, the mean for beliefs score is (M = 2.06, SD = 1.78) different with the mean for participants’ internet use score, it is reported (M = 19.78, SD = 8.30).

The tests used was Sharpiro Wilk test and sample size for all score of participants’ age range are 40 and the significant value for all score of participants’ age range is p < 0.05 except for the participants internet use aged 36-45 which is p > 0.05. Therefore only participants internet use aged 36-45 was not significantly deviated from the normal distribution and the rest of data was significantly deviated from the normal distribution.

ANOVA

Next, ANOVA was used to compare the participants age range which is 18-25, 36-45 and 46-55 with the score of participants’ beliefs. The result is not statistically significant. F (2,117) = 0.158, p > 0.05.

However, after we obtained the result of participants’ religious score, we found that the assumption of homogeneity is p > 0.05 which is 0.091. Therefore, it is not violated the assumption of homogeneity.

Next, the result of participants internet used was obtained and we found that the assumption of homogeneity is p < 0.05 which is 0.020. Therefore, it is violated the assumption of homogeneity. The robust test of Equality of Means (Welch) was conducted because it is preferable since we have violated the assumption.

The result of Robust test of equality means is not statistically significant because p < 0.05 which is 0.00 (welch)

Different for the score of participants’ beliefs, we found that the assumption of homogeneity is p > 0.05 which is 0.347. Therefore, it is not violated the assumption of homogeneity.

POST-HOC TEST

One-way independent ANOVA was conducted compare religious score between age range 18-25, 36-45 and 46-55. The results are not statistically significant, F (2,117) = 2.88, p = 0.6, with small effect size (n2= 0.047). Since the Levene’s test was not statistically significant, the Tukey HSD test result was reported and it was found that there are no significant differences in Religious score between all comparison of group. i.e., between 18-25 (M = 32.10, SD = 6.24) and 36-45 (M = 31.85, SD = 8.21, p = 0.983), between range age 18-25 and 46-55 (M = 34.95, SD = 4.13, p = 0.120). No statistically significant difference in religious score was also found between age range 36-45 and 46-55 (p = 0.082).

A one-way independent ANOVA was conducted to compare the internet use score between age range 18-25, 36-45 and 46-55. The results are statistically significant, F (2,117) = 37.78, p = 0.020, with a large effect size (ƞ2= 0.392). Since the Levene’s test showed a statistically significant result, the Tukey HSD test result indicated that there is a significant difference in the internet use score between the comparisons of groups. i.e., 18-25 (M = 16.38, SD = 7.91) age range and 36-45 (M = 27.10, SD = 4.919, p = 0.000) age range. However, there is no significant difference between age range 18-25 and 46-55 (M = 15.88, SD = 6.40, p = 0.937). In contrast, there is a statistically significant difference in the internet use score found between age range 36-45 and 46-55 (p = 0.000).

A one-way independent ANOVA was conducted to compare the Perceptual Belief score between participants aged 18-25, 36-45, 46-55. The results are not statistically significant, F (2,117) = 0.158, p > 0.05 which is 0.854 which have a small effect size (η² = 0.002). Since the Levene’s test was not statistically significant which is 0.0347, the Tukey HSD Test results were reported, and it was found that there are no significant differences in Perceptual Belief score between all level of Participant’s age i.e. Between the participant’s age in 18-25 (M= 2.18, SD= 1.933) and 36-45 (M= 2.05, SD= 1.552, p = 0.948 ).Between the participant’s age in 18-25 and participant’s age in 46-55 (M= 1.95, SD= 1.880, p= 0.841). No statistically significant difference in perceptual belief score between the participant’s age 36-45 is different between participant’s age in 46-55 (p= 0.966).

Reliability Analysis

Religious score

font-family:

a. Listwise deletion based on all variables in the procedure.

The scale of religious score was used to measure age range of participants. The total of 120 participants from age range 18-25, 36-45, 46-55 answer the scale which consist of 2 items. The Cronbach’s Alpha for 2 item was 0.309 showing low reliability.

- DESCRIPTIVE and INFERENTIAL ANALYSIS

FOURTH STEP & FIFTH STEP

The fourth step in the anlysation is to find descriptive and inferential analysis

- Firstly, we need to clink on the Analyze → Descriptive Statistics → Descriptive like in the picture below:

- Then, descriptive dialogue box will appear like in the image below:

-After that, click at the option and tick Mean, Standard Deviation, Minimum, Maximum, Kurtosis, Skewness and lastly Variable list.

Then, click Continue and OK

- Lastly, this will be the output of the descriptive result

For the fifth step, we will analyse our own inferential test that has been given by our instructor which is One-Way Independent ANOVA.

In order to conduct One-way Independent ANOVA, firstly we need to click at Analyze → Compare Means → One Way ANOVA:

-Then, oneWay ANOVA dialog box will appear:

- Next, click on Post Hoc,Tukey and Games - Howell then click Continue

-After that, click on Options and choose Descriptive, Homogeneity of Variances and Means plot then click Continue → OK

-Lastly, this is the output result of our analysis by using One-Way Independent ANOVA:

#descriptive

- SCREEN, CLEAN AND PREPARE DATA

THIRD STEP

The third step in hassling IBM SPSS data is to screen, clean and prepare the data. This is to check for any data entry error. To perform this stop, Frequencies command is used.

- Click Analyze > Descriptive Statistics > Frequencies > Move all the variables to the box on the right > OK

In the output, check for any missing data.

Next, compute the existing information of the variables.

Click Transform > Compute Variable.

In the Compute Variable dialogue box, the total score of Religious Attitude scale, Internet Use Scale and Perceptual Belief scale for each participants was computed.

Key in “TOTAL” in Target Variable > move the scales from the box under Type & Label into Numeric Expression > OK.

A new variable named as “TOTAL” was created in Variable View and the Measure of the data was changed.

New variable in Data View:

Afterwards, recheck for missing values in the Codebook Output:

Subsequently, the next step that had been done was to check the statistical assumption.

To do this, click Analyze > Descriptive Statistics > Explore.

Following that, Click on Plots button. When the Explore Plots dialogue pops out, select None, Histogram and Normality plots with tests > Continue > OK.

Then, the normality output of the normality test and the Histogram will appear:

- CODEBOOK

SECOND STEP

The second step after preparing the coding strategy is preparing the codebook.

This step is done by converting each data from each variables into an IBM SPSS-understandable format.

To prepare the codebook, click Analyze > Reports > Codebook.

Then, the dialog box below will be displayed

* Select and move all the variables into the Codebook Variables by highlighting the variables and clicking the arrow button between the two boxes.

- Next, click on the Output tab and untick all of the Variable Information except for Label, Value labels and Missing values

- Following that, click the Statistics tab and select all the boxes > OK

- Afterwards, the result of the Codebook will be displayed in the SPSS Output as shown below:

#codebook

- CODING STRATEGY

FIRST STEP

The first step that we performed after completion of collecting the data is to prepare the coding strategy which would be executed to liase the original data set in Microsoft Excel format with the SPSS data file.

- Firstly, double click the IBM SPSS icon to open the IBM SPSS and a blank data file will appear.

-Next, at the selection pane click File > Open > Data. Then,we will open an existing data file, click open and click data, click open and lastly click on file

- Afterwards, in the variable view, change the Name, Type, Decimals, Label and Measure of the data accordingly.

*Change the names of the variables

- Select the correct data type:

* Change the type of variables to numeric

- Change the decimals to 0 as there are no decimal scores in the data:

- Insert the proper Label for the variable

*Label the variables according to the order

- In the Values column, click the small blue box in the data cell to add a specific value to the variables:

- When a pop out box appears, insert the Value and label it accordingly.

* For instance:

Value: 1, Label: Male

as well as

Value: 2, Label: Female

.This step is repeated for each variables

- Change the Measures suitably:

* Change the measure of the variables according to the order

- Subsequently, a proper and complete data set will be produced as the outcome in both Data View and Variable View.

DATA VIEW:

VARIABLE VIEW:

#coding

ABOUT US

This blog was created as a requirement for our group project in psychological Statistics class (PSYC 1998) as instructed by our instructor, Dr. Nor Diana binti Mohd. Mahudin. Our aim for this project is to analyse a set of data by measuring it using IBM SPSS.

The focus of our study is to analyse the relationship between religiosity, internet use and perceptual belief of people who lives in Selangor. We have selected 40 residents from Selangor, between male and female at the age of 36-45 as our participants. Then, we distributed the questionnaires to respondent via manual and online by using google forms.

After collecting the data, we have been instructed to run the data collected with One-Way Independent ANOVA and we have been given three different established scales which is the IIUM Religosity Scale (IIUMReIS) by Mohd Mahudin, et al., (2016), the Online Disinhibition Scale (ODS) by Udris (2014), and the subscale of Magical Thinking (MT) from the Schizotypal Personality Questionnaire (SPQ) by Raine (1991). Next, we need to comprehend the output, scoring and interpretation.

Finally, after collecting all the data from the respondents, we have to analyse the data in three different analysation in IBM SPSS which are Descriptive Statistical Analyses, One-Way Independent ANOVA and the Reliability Analysis.

Student name list:

1. Aqila Camalya binti Mohamad Shukri 1622290

2. Izzah binti Jamil 1719542

3. Nur Farahani bt Mohd Apandi 1622780

4. Najihah Arman 1713572

#aboutus

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