Discussion:

The bulk of research on video games has largely focused on adolescents, teenagers and aggression. The most obvious and powerful change in games has been in their growing social nature. Most obvious and powerful change in games has been in their growing social nature. Game players had already been known to seek out game play in general for social reasons but for explicitly networked games, the attractions are the other players the relationships between them and their impact on out-of-game community and relationships. For gender-based research, it is imperative to begin considering game spaces in which players from both genders interact, rather than studying solo players in a lab and using gender as a post hoc control variable. In games, gender has again been employed as a basic demographic control, rather than as a dynamic element that shapes how players approach games, interact within them, and negotiate expectations. Gender research has almost entirely avoided the study of sexual relationships among gamers, but has occasionally examined family interactions focusing instead on hot social topics such as the displacement of homework, and health by gender.

A handful of recent studies have examined the social contexts of female game play. Female gamers—both young and old—who play frequently believe that games can be valuable spaces for socializing, including playing with friends and family as well as meeting new people via games and females are also more often drawn to gaming through offline social networks than through standard advertising, which tends to focus on a male point of view .Yet such work stresses the factors that bring female players to games, and does not scientifically explore how they play or think about playing once in games. Further, none of the research yet performed utilizes gender role theory to explore female game play, leaving the theory underdeveloped in terms of contemporary digitally based leisure activities.

From our study we can conclude that the four different independent variables were explained by certain number of factors as shown in component matrix. The table contains component loadings, which are the correlation between the variable and component. The table makes it easier to read the correlations in a meaningful way. The table has principle components that have been extracted. As one can see from the footnote that 4 components have been extracted. The components whose Eigen values are greater than 1.

From the table one can see that the values of all the components are not very clear and lies in the grey area. The components have not loaded properly accounting to the contribution of different factors. Therefore we performed the rotation and got the rotated component matrix which shows the factor loadings for each variable.

We went across each row to find out which variable is contributing highest for proper loading. When you see the age subtest it loaded highest on factor 1 with a value of .765. Similarly educational qualification loaded highest on factor 1 as well. The third question loaded max with factor 2 with a value of .576. Similarly the fourth question loaded with factors 1 and 3 with values corresponding to .403 and .540. The question with colour theme loaded with factor 4 and has a value .818. Likewise question sixth, seventh, eighth and ninth loaded with factor 2, factor 3, factor 2 and factor 3 and has values equivalent to .544, .651, .741 and .713 respectively. The rotation method used was Varimax with Kaiser Normalization and rotation converged in 19 iterations.

We are not using the component transformation matrix table as it has to do with SPSS performing the orthogonal rotation that was asked. Then Regression was run on the data.

This table simply states the variables in the model and the selection method chosen. From the model summary table we can see that the correlation and the r-square and indicates how much is explained. We are not worried about the annova box.

The final box labelled Coefficient gives the results of the analysis. Each columns is explained below:

Unstandardized Coefficients B: This shows the value and the numbers in the linear regression equation.

The relationship between REGR factors and the dependent variable “how many factors have you spend playing video games in the last fortnight?” are .336, .115, .363 and .346 respectively

Unstandardized Coefficients Std. Error: This is the standard error for the coefficients- it is used in the calculation of significance.

T test: This is the t-test to see if the coefficients are significantly different from 0. A value over 1.96 indicates significance at the 5% level.

Sig: This is the p-value. If this is under 0.05 then the variable is significant. The values we have here are .005, .324, .002 and .004 which implies that there is a significant relationship between dependent variable and other REGR factor scores except REGR factor 2 which is over 0.05. Thus for the question item “How many hours have you spend in playing video games in the last fortnight?” significant factors are demography, feminine nature and game design.

Similarly we have performed regression for other dependent variables “In the last fortnight maximum how long have you spent in playing a videogame in one sitting?” and “How many different videogames have you ever played?” and have found significant factors gender role & stereotype along with feminine nature having .044 and .005 sig values and gender roles and stereotype with .048 sig value.

Taken together, these findings affirm the predictive power of gender role theory, and highlight the importance of including gender as an independent variable in future work among social gamers.