{"id":23718,"date":"2026-01-07T12:20:49","date_gmt":"2026-01-07T06:50:49","guid":{"rendered":"http:\/\/www.sachdevajk.in\/?p=23718"},"modified":"2026-01-07T12:20:49","modified_gmt":"2026-01-07T06:50:49","slug":"anova-based-comparison-ratings-for-game-reviews","status":"publish","type":"post","link":"http:\/\/www.sachdevajk.in\/?p=23718","title":{"rendered":"ANOVA Based Comparison Ratings for Game Reviews"},"content":{"rendered":"

ANOVA Based Comparison Ratings for Game Reviews<\/span><\/b><\/p>\n

Name- Ansh Bhalekar (021331025065)<\/span><\/b><\/p>\n

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Introduction<\/span><\/p>\n

The gaming industry has experienced rapid growth, accompanied by an increase in the number of online and offline game review platforms. Listener or viewer ratings of game reviews play a crucial role in shaping public perception, influencing purchase decisions, and determining the credibility of reviewers. Different games often receive varied ratings based on factors such as gameplay, graphics, storyline, and overall user experience. To statistically examine whether these differences in ratings are significant, Analysis of Variance (ANOVA) is an appropriate technique. This study applies ANOVA to compare listeners\u2019 ratings for different game reviews and determine whether the mean ratings differ significantly across games.<\/span><\/p>\n

Objectives of the Study<\/span><\/b><\/p>\n

1.\u00a0\u00a0\u00a0 <\/span><\/span><\/span>To analyze listeners\u2019 ratings for different game reviews.<\/span><\/p>\n

2.\u00a0\u00a0\u00a0 <\/span><\/span><\/span>To compare the mean ratings of multiple games using ANOVA.<\/span><\/p>\n

3.\u00a0\u00a0\u00a0 <\/span><\/span><\/span>To identify whether statistically significant differences exist among game review ratings.<\/span><\/p>\n

4.\u00a0\u00a0\u00a0 <\/span><\/span><\/span>To support objective decision-making regarding game performance based on listener feedback.<\/span><\/p>\n

\u00a0<\/span>Literature Review<\/span><\/b><\/p>\n

Previous studies have highlighted the importance of user-generated ratings in digital entertainment markets. According to Chevalier and Mayzlin (2006), online reviews significantly affect consumer demand and perception. Research by Hu, Pavlou, and Zhang (2017) suggests that statistical techniques such as ANOVA are effective in comparing consumer ratings across multiple products. In the context of gaming, listener and viewer ratings are often used as performance indicators, and comparative analysis helps developers and marketers understand audience preferences.<\/span><\/p>\n

Data Collection<\/span><\/b><\/p>\n

The data for this study consists of listeners\u2019 ratings collected for different game reviews. Ratings were obtained using a structured rating scale (for example, 1 to 5), where listeners evaluated the overall quality of the game review. The data was collected from a sample of listeners who had experienced or listened to reviews of multiple games. Each game represents a separate group for analysis.<\/span><\/p>\n

Data Analysis<\/span><\/b><\/p>\n

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ANOVA<\/span><\/p>\n<\/td>\n

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Source of Variation<\/span><\/p>\n<\/td>\n

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SS<\/span><\/p>\n<\/td>\n

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df<\/span><\/p>\n<\/td>\n

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MS<\/span><\/p>\n<\/td>\n

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P-value<\/span><\/p>\n<\/td>\n

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Between Groups<\/span><\/p>\n<\/td>\n

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157.256<\/span><\/p>\n<\/td>\n

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3<\/span><\/p>\n<\/td>\n

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52.4188<\/span><\/p>\n<\/td>\n

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7.370<\/span><\/p>\n<\/td>\n

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0.000<\/span><\/p>\n<\/td>\n

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2.664<\/span><\/p>\n<\/td>\n<\/tr>\n

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Within Groups<\/span><\/p>\n<\/td>\n

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1080.974<\/span><\/p>\n<\/td>\n

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152<\/span><\/p>\n<\/td>\n

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7.111<\/span><\/p>\n<\/td>\n

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Total<\/span><\/p>\n<\/td>\n

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1238.230<\/span><\/p>\n<\/td>\n

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155<\/span><\/p>\n<\/td>\n

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Hypothesis Testing<\/span><\/b><\/p>\n