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Figure 4. Bug strengths and weaknesses regressed on plays, linear and quadratic models including outliers Note. Linear (solid line) and quadratic (dotted line) models of bug strengths and weaknesses regressed on plays, including outliers (see Table 2), are overlaid on a scatterplot of the raw data up to 1000 plays. In the scatterplot, bug strengths and weaknesses are sum scores of observed responses.

Figure 4 Bug strengths and weaknesses regressed on plays, linear and quadratic models including outliers Note. Linear (solid line) and quadratic (dotted line) models of bug strengths and weaknesses regressed on plays, including outliers (see Table 2), are overlaid on a scatterplot of the raw data up to 1000 plays. In the scatterplot, bug strengths and weaknesses are sum scores of observed responses.

Related Figures (6)

Figure 1. Raincloud plots of reported plays by whether participants recorded their plays Note. For participants who did and did not record their plays of the game Hive, reported plays are visualized (adaptec from Allen, Poggiali, Whitaker, Marshall, & Kievit, 2019) as a density plot, boxplot (thick line: median; upper and lower hinges: 25th and 75th percentiles), and scatterplot (raw data). Data were log jo-transformed before plotting.
Figure 1. Raincloud plots of reported plays by whether participants recorded their plays Note. For participants who did and did not record their plays of the game Hive, reported plays are visualized (adaptec from Allen, Poggiali, Whitaker, Marshall, & Kievit, 2019) as a density plot, boxplot (thick line: median; upper and lower hinges: 25th and 75th percentiles), and scatterplot (raw data). Data were log jo-transformed before plotting.
Figure 2. Scatterplots, density plots, and correlations of study variables Note. Scatterplots are depicted in the lower half, density plots on the diagonal, and zero-order Pearson correlation in the upper half. Bug strengths and weaknesses are sum scores of observed responses, rather than estimated fact« scores of the latent variable used in analyses. Mental model matching. Mental model matching was measured with two novel instruments that had
Figure 2. Scatterplots, density plots, and correlations of study variables Note. Scatterplots are depicted in the lower half, density plots on the diagonal, and zero-order Pearson correlation in the upper half. Bug strengths and weaknesses are sum scores of observed responses, rather than estimated fact« scores of the latent variable used in analyses. Mental model matching. Mental model matching was measured with two novel instruments that had
Table 1. Regression coefficients for BoardGameArena Elo rating on plays: linear and quadratic models Note. Plays were centered on 1; intercepts are predicted values for participants who have played Hive once. After centering, plays were divided by 100 for estimation and reporting. Because models are just-identified, no global fit Statistics are available. See Tables S9—S12 in Supplement 3 for parameter estimates with confidence intervals. b = unstandardized, B = fully standardized.
Table 1. Regression coefficients for BoardGameArena Elo rating on plays: linear and quadratic models Note. Plays were centered on 1; intercepts are predicted values for participants who have played Hive once. After centering, plays were divided by 100 for estimation and reporting. Because models are just-identified, no global fit Statistics are available. See Tables S9—S12 in Supplement 3 for parameter estimates with confidence intervals. b = unstandardized, B = fully standardized.
Table 2. Regression coefficients for mental model matching on plays: linear and quadratic models Note. Plays were divided by 100 for estimation and reporting. Bugs: bug strengths and weaknesses. All models had good global fit, x? p > .19. In Supplement 3, see Table 1 for global model fit indices and Tables S5—S8 for complete parameter estimates with confidence intervals. b = unstandardized. For Bugs, B = fully standardized; for all other, ordinal outcome variables, 8 = standardized x.
Table 2. Regression coefficients for mental model matching on plays: linear and quadratic models Note. Plays were divided by 100 for estimation and reporting. Bugs: bug strengths and weaknesses. All models had good global fit, x? p > .19. In Supplement 3, see Table 1 for global model fit indices and Tables S5—S8 for complete parameter estimates with confidence intervals. b = unstandardized. For Bugs, B = fully standardized; for all other, ordinal outcome variables, 8 = standardized x.
Figure 3. Elo regressed on plays, linear and quadratic models including outliers Note. Linear (solid line) and quadratic (dotted line) models of Elo regressed on plays, including outliers (see Table 1), are overlaid on a scatterplot of the raw data up to 1000 plays.
Figure 3. Elo regressed on plays, linear and quadratic models including outliers Note. Linear (solid line) and quadratic (dotted line) models of Elo regressed on plays, including outliers (see Table 1), are overlaid on a scatterplot of the raw data up to 1000 plays.
Figure 5. Estimated probabilities of responses to strategy understanding questions Note. Expected probability of selecting each successively better response to strategy understanding items, from worst ( to best (3 or 4, depending on the item), as a function of plays in the linear model including outliers (see Table 2). Dashe lines are non-symmetric bootstrapped 95% confidence intervals with 5000 bootstraps.
Figure 5. Estimated probabilities of responses to strategy understanding questions Note. Expected probability of selecting each successively better response to strategy understanding items, from worst ( to best (3 or 4, depending on the item), as a function of plays in the linear model including outliers (see Table 2). Dashe lines are non-symmetric bootstrapped 95% confidence intervals with 5000 bootstraps.
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