Multiple Regression Essay

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Abstract

The total points in a game have been linked with numerous factors. The study evaluated whether there existed any relationship between total points scored and factors such as gender, age group, fixed – win number of picks, and functional impulsivity. The study used a sample of 170 data sets from IST-4015 dataset. The data was analysed using SPSS version 22. The method used to analyse the relations was multiple regression. The results confirmed that there was a linear relationship between total points, Fixed-Win Number of Picks, gender, age group and Functional Impulsivity. 

Multiple Regression

There has been a debate as to what factors influences the total points in any given game. To find out the solution to this question , a researcher carried out the research with the aim of to finding out whether there exist a linear relationship between total points , gender , age group, FWcorrect, DWcorrect, Fixed-Win Number of Picks, Functi-Imp , DL-new and gender, age group and Functional Impulsivity . 

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Hypothesis

 Null hypothesis:

There is no relationship between total points, FWcorrect, DWcorrect, Fixed-Win Number of Picks   , Functi-Imp , DL-new and gender ,age group and Functional Impulsivity .

Alternative hypothesis

There is a relationship between total points, FWcorrect, DWcorrect, Fixed-Win Number of Picks   , Functi-Imp , DL-new and gender ,age group and Functional Impulsivity .

Results

Using the IST_4015 report data, the researcher used SPSS version 22 to carry out the study. Since the analysis involved relationship between more than two variables, the researcher used multiple regression. The dependent variable was the total points of the game while the independent variables were FWcorrect, DWcorrect, Fixed-Win Number of Picks   , Functi-Imp , DL-new and gender ,age group and Functional Impulsivity .

Before the analysis, the multiple regression assumptions were tested. Firstly, the assumption that the dependent variable be measured on continuous scale was valid as evidenced by the total point’s data. Secondly, the assumption that the data be free of outliers was also valid as only very few insignificant data values were off as evidenced by the total point’s boxplot (Figure 1). In addition, the assumption that the dependent variable be normally distributed was valid as evidenced by the total point’s histogram (Figure 2).Furthermore, assumption of existence of linearlity was also affirmed by the total points overlay scatterplot (Figure 3).  Lastly, the independent variables were independently observed as the secondary data was gathered from credible source.

From the results, an F (10,159) = 1418.298, p = .000 (Table 2), there was statistical evidence that there existed a linear relationship between total points , FWcorrect, DWcorrect, Fixed-Win Number of Picks, Functi-Imp, DL-new and gender, age group and Functional Impulsivity. Age group, Decreasing-Win Number of Picks , Functional Impulsivity  and Dys-imp were all negatively related to total points as indicated by the coefficient values of -10.88, -8.25, -1.498, -9.16 respectively while gender, FWcorrect, DWcorrect Fixed-Win Number of Picks   DL-new, Funct-imp were all positively related as evidenced by their coefficients values of 10.93, 207.18, 305.19, 0.067, 1.87 and 13.68 respectively ( Table 3). The model was found to be very accurate for total points prediction as showed by the R2=.994 ( Table 1) which implied that the model is able to explain 99.4% of the total points variations with only less than 1% remaining unexplained. 

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Discussion

Based on theory there is evidence that total points of a game is related to gender, age group, number of fixed win,  functional impulsivity. According to Desai, Krishnan-Sarin, Cavallo, and Potenza (2010) total points in games is higher in boys compared to girls. Gaming is perceived to be more appealing to boys, while particularly attractive to girls with some qualities. Desai, Krishnan-Sarin, Cavallo, and Potenza (2010) reveal that among girls, gaming could exert a positive impact on their mood, while among boys, gaming results in addiction that is linked with beneficial or pro-social behaviour. Unlike women, men in games enjoy competition. Women, on the other hand enjoy cooperation. Additionally, women are more engaged in games as casual players, while men participate as shooters and role players. The implication is that women are reluctant to take up the identity as gamers owing to their self-perceived lack of expertise and time commitment, or less interest in hard core games. 

Total points of a game relates to age group. Males and females tend to play the games that appeal to their base instincts. True Gaming (2016) reports that most of the gaming women fall within the age bracket of 18 years and above. These women constitute 36% of the gaming women while 17% of the gaming males were in the age bracket of less than 18 years.  The findings also indicate that the predominant audience for video games are males at 78%, with 25% of aged between 15-19 years and constituting the largest section of playing population. On the contrary, women lagged behind as gaming audiences constituting 22% with the majority being aged between 20-24 years. Consequently, the total points for males are often higher than those of females.

The number of fixed wins in games relate to the total points earned. In the card game of poker in the case of a player winning two pots in a row the stakes double. The implication is more points for the player. This rule is called ‘kill game’ (Fraser, 2015). 

From the study, functional impulsivity positively relates to earning higher points. According to Blinka, Skarupova, and Mitterova (2016), higher impulsivity is a risk factor for one to become a pathological gamer. Additionally, gaming addiction is linked to impulsivity, while high impulsivity levels was the only predicting factor for problematic gaming. Compared with other addicts, problematic gamers are particularly younger, demonstrated high levels of anxiety, and were less extroverted. Functional impulsivity was found to be inversely proportional to age, as it decreased as gamers tended towards adulthood, while taking on different structural features with age Blinka, Skarupova, and Mitterova (2016). Two predictors of obsessive passion that characterizes functional impulsivity is obsessive passion whereas time spent playing was only predicted through harmonious passion. 

Implication of the Linearity 

The linearity between total points scored and factors like age, gender, functional impulsivity, and number of fixed wins can be used in designing sustainable procedures and efforts to close the gender gap in gaming. For instance, women could be encouraged to opt for less casual games despite being more appealing to them. Additionally, the linearity can be used to explain the persistence in gender variations in the design choices where girls and boys learnt to creation their own educational games. 

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How the Research Can Be Expound

This research can be expounded to explain the gendered behaviour patterns in gaming like why very few women are attracted to competitive games, and why they perform worse compared to men. Attaining more accurate data should not focus on self-reported gender data, but on data obtained from real game play.

Conclusion

The report discussed how different factors, gender, age group, number of fixed win, and functional impulsivity, determine the total points earned in a game. The linear relationship between total points earned and gender explained the low total points among women owing to their spending less time on games compared to men. Additionally, total points earned in a game were dependent on functional impulsivity as one’s passion dictated their anxiety and need to win. Age determined total points earned as younger male and females participated in games compared to adults.

Appendix

 

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .994a .989 .988 61.495
a. Predictors: (Constant), gender, values.Decreasing-Win Number of Picks , age_gp, FI_new, Dys_Imp, values.FWcorrect, values.DWcorrect, values.Fixed-Win Number of Picks  , Funct_Imp, Dysfunctional Impulsivity 

 

Table 1: Total points model summary

ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 53635203.719 10 5363520.372 1418.298 .000b
Residual 601283.928 159 3781.660
Total 54236487.647 169
a. Dependent Variable: values.totalpoints
b. Predictors: (Constant), gender, values.Decreasing-Win Number of Picks , age_gp, FI_new, Dys_Imp, values.FWcorrect, values.DWcorrect, values.Fixed-Win Number of Picks  , Funct_Imp, Dysfunctional Impulsivity 

 

Table 2: Total points model ANOVA output

Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -1620.477 90.313 -17.943 .000
age_gp -10.883 9.807 -.009 -1.110 .269
values.FWcorrect 207.175 3.960 .579 52.314 .000
values.DWcorrect 305.188 3.366 .882 90.666 .000
values.Fixed-Win Number of Picks   .067 .101 .008 .661 .510
values.Decreasing-Win Number of Picks  -8.253 .173 -.543 -47.841 .000
FI_new -1.498 1.953 -.015 -.767 .444
Dysfunctional Impulsivity  1.866 2.537 .015 .736 .463
Dys_Imp -9.161 13.569 -.014 -.675 .501
Funct_Imp 13.677 13.351 .020 1.024 .307
gender 10.928 11.425 .008 .957 .340
a. Dependent Variable: values.totalpoints

Table 3: Total points coefficient table output

Figure 1: A histogram of Total points 

 

Figure 2: Total points boxplot

 

Figure 3: Scatterplot of total points versus gender, age group, FWcorrect, DWcorrect and Funct-lmp 

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  1. Blinka, L., Skarupova, K., & Mitterova, K. (2016). Dysfunctional impulsivity in online gaming addiction and engagement. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 10(3).
  2. Desai, R., Krishnan-Sarin, S., & Cavallo, D. P. (2010). Video game playing in high school students: health correlates, gender differences and problematic gaming. Pediatrics, 126(6), e1414–e1424.
  3. Fraser, K. (2015). Total card games! : the biggest and best collection of solo & group card games. USA: lulu.com.
  4. Gerber, S. B., & Finn, K. V. (2013). Using SPSS for Windows: Data Analysis and Graphics. Berlin: Springer.
  5. Leech, N. L., Barrett, K. C., & Morgan, G. A. (2005). SPSS for Intermediate Statistics: Use and Interpretation. London: Psychology Press.
  6. Meyers, L. S., Gamst, G., & Guarino, A. (2012). Applied Multivariate Research: Design and Interpretation. California: SAGE Publications.
  7. Pallant, J. (2010). SPSS Survival Manual: A step by step guide to data analysis using SPSS. Crows Nest: Allen & Unwin.
  8. True Gaming. (2016). Gender and computer game players: who seems to play what? a wall of text linkdump. USA: Reddit.
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