Descriptive analyses were conducted on the affective measures (positive mood, negative mood, activation and deactivation) for the pre and post-test scores for all participants (see Table 18 below).
Table 18: Descriptive analyses for pre and post test affect measures for all participants
*p < .05 ** p < .001 Four paired samples t-tests were conducted to examine the differences between pre and post test gameplay on the four affect measures. The analysis revealed that positive affect (t (55) = -6.04, p < .001) and activation (t (55) = -6.29, p < .001) were
Measure Time Pre-test Post-test M SD M SD Positive Affect 3.11** .64 3.60** .67 Negative Affect 1.22 .33 1.19 .26 Activation 2.36** .48 2.82** .47 Deactivation 2.59** .47 2.04** .57
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significantly higher after gameplay. Deactivation was significantly lower after gameplay (t (54) = 8.17, p < .001). No significant differences were found in negative affect between pre and post test conditions (t (55) = .86, p = .40), although the post- test mean negative affect score was slightly lower than at pre-test.
Descriptive analysis was conducted on the pre and post test affect scores and for flow between game conditions (see Table 19 below). Four mixed between-within ANOVAs were conducted for each of the affect measures to examine the changes in affect scores between pre and post test.
Table 19: Descriptive analysis of pre and post-test affect and flow scores between game conditions
Note: No pre-test measures of flow were taken
Positive affect
A mixed design ANOVA (2x3x3) was conducted to examine the impact of game condition (highly violent versus mildly violent), flow (low, medium and high) and trait aggression (low, medium and high) on changes between pre and post test measures of positive affect. Participants were divided into three groups according to both their flow (low, medium, high) and aggression scores (low, medium, high). The main effect comparing pre and post-test positive affect was significant, [F (1, 43) = 7.50, p < .01, partial eta squared = .15], suggesting a significant increase in positive affect scores after gameplay (Table 18). There was a significant interaction effect between flow and changes to positive affect (Wilks Lambda = .85, F (2, 43) = 3.82, p < .05, partial eta squared = .15), indicating that experiences of flow in gameplay were
Measure Half Life 2 Halo 3
Pre-test Post-test Pre-test Post-test
M SD M SD M SD M SD Positive Affect 2.99 .54 3.57 .65 3.21 .69 3.61 .70 Negative Affect 1.30 .43 1.24 .32 1.16 .23 1.15 .19 Activation 2.29 .43 2.84 .46 2.42 .52 2.81 .49 Deactivation 2.73 .49 2.22 .62 2.48 .42 1.87 .50 Flow 3.91 .33 3.83 .40
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associated with increases in positive mood (see Figure 2 for interaction6). This indicates that enhanced flow experiences (i.e. high and medium flow) are associated with the greatest increases in positive affect. Interestingly, positive affect scores in the low flow group showed a different pattern compared to the medium and high flow groups. Specifically, positive affect scores decreased from pre to post test in the low flow group. Trait aggression (F (2, 43) = .16, p = .85) and game condition (F (1, 43) = .59, p = .45), however, did not show any significant main effects or interactions with changes in positive affect.
A one-way ANOVA was conducted to assess any between condition effects of flow condition on positive affect scores both at pre and post test. Results showed that there were no significant differences between flow conditions on positive affect at pre-test (F (2, 54) = 2.72, p = .075), whereas there were significant differences at post-test (F (2, 54) = 3.26, p < .05). Specifically, post-hoc comparisons revealed the differences were between the low and high flow groups (MD = 1.18, p < .05).
Figure 2: Interaction of flow on pre to post test measures of positive affect
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Pre-test positive affect means by flow condition: low = 3.55; medium = 3.03; high = 3.60 Post-test positive affect means by flow condition: low = 2.95; medium = 3.54; high = 4.13
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Negative affect
A mixed design ANOVA (2x3x3) was conducted to examine the impact of game condition (highly violent versus mildly violent) flow (low, medium and high) and trait aggression (low, medium and high) on changes between pre and post test measures of negative affect. Participants were divided into three groups according to both their flow (low, medium, high) and aggression scores (low, medium, high). The main effect comparing pre and post-test negative affect was non-significant, [F (1, 43) = .62, p = .44, partial eta squared = .01], suggesting no significant changes in negative affect scores after gameplay (Table 18). There were no significant main effects or interactions between flow and changes to negative affect (Wilks’ Lambda = .98, F (2, 43) = .36, p =.70, partial eta squared = .02), trait aggression (Wilks’ Lambda = .96, F (2, 43) = .82, p = .45, partial eta squared = .04), or game condition (Wilks’ Lambda = 1.00, F (1, 43) = .05, p = .82, partial eta squared = .001).
Activation
A mixed design ANOVA (2x3x3) was conducted to examine the impact of game condition (highly versus mildly violent), flow (low, medium and high) and trait aggression (low, medium and high) on changes between pre and post test measures of activation. Participants were divided into three groups according to both their flow (low, medium, high) and aggression scores (low, medium, high). The main effect comparing pre and post-test activation was significant, [F (1, 43) = 11.95, p <.01, partial eta squared = .22], indicating a significant increase in activation scores after gameplay (see Table 18). There was a significant interaction between flow and changes in activation (Wilks’ Lambda = .76, F (2, 43) = 6.68, p <.01, partial eta squared = .24), indicating that experiences of flow in gameplay results in increased activation (see Figure 3 for interaction effect7). This suggests that enhanced flow experiences are associated with the greatest changes in activation from pre to post test. However, similarly to the changes in positive affect scores for those in the low flow group, activation scores decreased from pre to post test in the low flow group. No significant main effects or interactions were observed for trait aggression (Wilks’ Lambda = .99, F (2, 43) = .24, p = .79, partial eta squared = .01), or game condition
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Pre-test activation means by flow condition: low = 2.60; medium = 2.34; high = 2.40 Post-test activation means by flow condition: low = 1.90; medium = 2.80; high = 3.17
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(Wilks’ Lambda = 1.00, F (1, 43) = .02, p = .88, partial eta squared = .001) on changes in activation after gameplay.
A one-way ANOVA was conducted to assess any between condition effects of flow condition on activation scores both at pre and post test. Results showed that there were no significant differences between flow conditions on activation at pre-test (F (2, 54) = .28, p = .759), whereas there were significant differences at post-test (F (2, 54) = 6.87, p < .01). Specifically, post-hoc comparisons identified differences between low and medium (MD = .90, p < .05), and low and high flow groups (MD = 1.27, p < .01).
Figure 3: Interaction of flow on pre to post test measures of activation.
Deactivation
A mixed design ANOVA (2x3x3) was conducted to examine the impact of game condition (highly versus mildly violent), flow (low, medium and high) and trait aggression (low, medium and high) on changes between pre and post test measures of deactivation. Participants were divided into three groups according to both their flow (low, medium, high) and aggression scores (low, medium, high). The main effect comparing pre and post-test deactivation was significant, [F (1, 43) =28.87, p <.001,
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partial eta squared = .41], indicating a significant decrease in deactivation after gameplay (see Table 18). There was a significant interaction of game condition (Wilks’ Lambda = .82, F (1, 43) = 9.15, p < .01, partial eta squared = .18), indicating that there was greater deactivation following gameplay of Halo 3 compared with Half
Life 2 (see Figure 4 for interaction effect). No main effects or significant interactions
were observed for flow (Wilks’ Lambda = .91, F (2, 43) = 2.02, p =.15, partial eta squared = .09), or trait aggression (Wilks’ Lambda = 1.00, F (2, 43) = .02, p = .98, partial eta squared = .001) on changes in deactivation.
An independent samples t-test was conducted to assess the between condition effect of game condition on deactivation at both pre and post test. Results showed that there were no significant differences between the game conditions in deactivation scores at pre-test (t (53) = 1.96, p = .06). At post-test, however, significant differences between the game conditions on deactivation scores were observed (t (54) = 2.30, p < .05).
Figure 4: Interaction of game condition on pre to post test measures of deactivation.