Data Analysis Procedure

The data analysis procedure includes two main phases: Descriptive statistical data analysis and qualitative data analysis. Also reliability analysis is also provided in this part.

3.5.1. Data Entrance and Pre-Analysis Operations

For data collected from by the help of the questionnaire, first, the variable names and variable types were determined (nominal, ordinal, or scale) and variables were created. Then the data were coded into SPSS program by using generally numbers for the options of the

questions that have more than one option. A code number was assigned to each respondent, which identifies students with numbers and the initial character of the university name, such as first respondent from the Ankara University was identified by A-1. For other universities, these initials were used: Gazi University (G), Hacettepe University (H), Middle East Technical University (M). Besides, the participants in the interview was indicated by additional letter, I (Example: AI-1 represented Ankara University participant who was interviewed as well as filled the questionnaire)

After coding multiple choice and likert scale type questions, the items that required different types of responses were coded differently. To begin with the question 9 in the Part I of the questionnaire two additional choices were coded, according to responses written in the ‘others’ choice, which are “in work place” and “in friends’ places”. Since more than one option can be selected by the subjects, one participant have more than one frequency contribution to the overall frequency of each different place where they use computers.

Other questions that have different data coding process were the questions 36 and 37 in the Part I, which are game and game type preferences questions. In question 36, according to responses, each game name was given a code number and then coded. Because the same situation of selecting more than one option was the case, one participant have more than one frequency contribution to the overall frequency of each different game. Then mostly preferred games were differentiated by using descriptive analysis.

In question 37 the problem faced during the coding data was that, many students wrongly responded the question. While question required students to rank their mostly preferred game type, 26 (28,9%) participants out of 90 who selected some of the game type options did not provide any rank and only selected their preferred game types. Besides the remaining 26 participants selected ‘I do not play games’ option. Since the data analysis would be defected, this question was analyzed by using the same method described for the question 36.

Also some other modifications were made on the data for the question 7 of Part I. Cumulative GPA of Ankara University was converted to 4-scale from 100-scale.

3.5.2. Descriptive Analysis

For categorical variables, the frequencies were calculated to describe the results of the study. Moreover, percentages were calculated and the mode for each categorical variable was revealed. By using SPSS, bar charts were provided to better illustrate the general view of these variables, regarding their frequencies. Mode and median representations were provided as well.

For quantitative or continuous variables, again frequency distributions and percentile ranks were used. Some statistical indices were computed as “measures of central tendency” (mean, median, mode), “measures of variability” (standard deviation). Graphical forms were also utilized.

3.5.3. Reliability Analysis

Before conducting reliability analysis, for some variables, the scale were reversed and recoded in another variable, because these variables were in opposite directions that the researcher wanted to investigate (Question numbers: 25,29,33,35 in Part I). Also the missing questions were managed by using “series mean” method in the SPSS which replace the mean values of the whole data in a variable with the missing values in that variable data. The mean values were coded in integer numbers by rounding the mean number (See Table 3.10). Due to the fact that some items in the questionnaire have different metrics, the data of the variables were standardized by converting in to Z-scores in different variables.

Table 3.10. Missing fields detected in the questionnaire data for only quantitative data entry

Question no. Subjects Replaced with # of missing

p1-q4 AI-3, G-19 3 2 p1_q16 M-26 4 1 p1_q20 HI-2 1 1 p1_q21 HI-1 3 1 p1_q25 M-14 3 1 p1_q27 M-16 3 1 p1_q29 HI-3 2 1 p1_q33 H-29 3 1 p1_q35 H-33 3 1 p2_q2 HI-3 3 1 p2_q13 G-20 3 1 Total: 12

Total : 6264 field. Empty: 12 field. Percentage Empty: 0,19

3.5.4. Qualitative Data Analysis

Qualitative data analysis was conducted regarding the content analysis explained by Yıldırım and Şimşek (2000) as: The data were coded, themes were found, the data were organized and defined according to the codes and themes, and interpretations were made. This process was also described by the steps of Miles and Huberman (1994) as ‘data reduction’, ‘data display’ and ‘conclusion drawing and verification’ (p. 10). To illustrate further in detail, for interview raw data, transcribed records were organized according to research questions firstly. Then they were summarized into shorter statements. Themes were determined and the statements were coded regarding these themes. The answers obtained during the interview sessions were tabulated and frequency information was provided along with this qualitative data. For the open-ended type questions in the Part II- question 20,21, the same procedure was used.