Impact of instructions on Generation and Selection

To evaluate whether participants adhered to the instructions of each experimental condition we estimated the trajectories of Generation and Selection throughout the four phases of the experiment to compare them across the conditions (see also Annexe 11 for

32 Confirmatory factor analysis showed the good fit of a one-factor model: χ²=18.9, df=7, RMSEA=.089 with 90% CI=[.042-.139], SRMR=.045, CFI=.96 (two residual correlations were set free, between items 2 and 3—

intensity items—and between items 5 and 6—contests and public presentations items).

additional testing of the impact of the instructions). We estimated the trajectories of Generation and Selection using the Latent Curve Model (LCM; Meredith & Tisak, 1990).

LCMs are specifically constrained structural equations models or, equivalently, multilevel models (for further details, see Ghisletta & Lindenberger, 2004). For a group of individual trajectories the LCM postulates both a common, average time-based trajectory and individual deviations from this average trajectory. We estimated straight-line trajectories, defined by an intercept (value at Phase 1) and a slope (change in value between two adjacent phases). In the end, the LCM estimates two so-called fixed effects (for the intercept and the slope), which describe the average trajectory, and so-called random effects (variance of intercept and of slope and covariance of the two), to capture the individual deviations. Finally, a so-called residual variance estimates the amount of error, that is, the discrepancy between the individual straight-line trajectories and the actual data points. In this application we focus on the mean intercept and slope estimates across the experimental conditions, as they represent the impact of the instructions on the average trajectories of Generation and Selection.

To implement LCMs separately on Generation and Selection, we grouped the nine conditions according to their trajectory across all phases of the experiment. We hence created four patterns: (1) ‘High Flat’, where we expected a high intercept and zero slope; (2) ‘Low Flat’, where we expected a low intercept and zero slope; (3) ‘Increase’, where we expected a low intercept and a positive non-zero slope; (4) and ‘Decrease’, where we expected a high intercept and a negative non-zero slope. The condensed LCM results are presented in Table 9 (corresponding figures of these estimations are available in Annexe 12 and Annexe 13).

We also applied the LCM to the control group, for which no particular instructions were provided. For Generation, the pattern of the control group was ‘High Flat’ in nature:

people in this group naturally started at a high level of Generation and maintained it throughout the task. For Selection, the control group’s pattern started lower and was characterized by a marked increase. It was important to include these natural patterns of the creativity sub-processes because they provided a baseline against which to judge the experimental effects on the sub-processes trajectories.

The experimental effects for Generation appeared quite successful: the mean slope was non-significant for the ‘High Flat’ group, positive for the ‘Increase’ group, and negative for the ‘Decrease’ group. However, contrary to expectations, the slope of the ‘Low Flat’

group was negative. To formally test the difference in the mean slope across these sets of conditions we computed multiple-group LCMs, which allow enforcing equality constrains on the same parameter (e.g., mean slope) across different groups. This equality can be

statistically tested with a likelihood ratio test (difference in chi square statistic for one degree of freedom). We found that the mean slope of the ‘Low Flat’ was not significantly different from the one of the ‘Decrease’ group (Δχ²=0.94, df=1, p=.33). These two groups differed, however, in their mean intercept (Δχ²=5.43, df=1, p=.02). Moreover, the mean intercept of the

‘Low Flat’ group was also significantly different from the analogous parameter in the ‘High Flat’ group (Δχ²=4.02, df=1, p=.045). Finally, the ‘Increase’ and ‘Decrease’ groups were different in both their intercepts (Δχ²=15.16, df=1, p<.0001) and their slopes (Δχ²=32.16, df=1, p<.0001).

Table 9. Results of latent growth model for each experimental group.

Generation Selection

Group n Intercept Slope n Intercept Slope

Control 14 3.34 / ns ns / ns 14 2.45 / ns 0.55 / ns High flat 72 2.88 / 0.48 ns / 0.10 67 2.87 / 0.67 0.30 / 0.07 Low flat 19 2.53 / ns -0.23 / ns 19 2.24 / 0.90 0.20 / ns Increase 29 2.27 / ns 0.35 / 0.12 33 2.19 / ns 0.57 / ns Decrease 31 2.99 / ns -0.32 / ns 32 2.87 / ns ns / ns

Note. Mean estimates / variance estimates. Patterns regrouping for Generation: High Flat = A1, A2, B3, and B4 (cf. Figure 13); Low Flat = A3; Increase = B2 and C1; Decrease = B1 and C2. Patterns regrouping for Selection:

High Flat = A1, A3, B1, and B2 (cf. Figure 13); Low Flat = A2; Increase = B4 and C2; Decrease = B3 and C1.

All parameters are significant at the .05 level unless specified ns (non-significant); n = participants per group.

Overall model adjustment for Generation model: χ2=63.97, df=40; RMSEA=.13 with 90% CI=[.068;.19], SRMR=.20, CFI=0.78. Overall model adjustment for Selection model: χ2=81.1, df=40; RMSEA=.17 with 90%

CI=[.12;.23], SRMR=.21, CFI=0.63.

For Selection, the Control group was characterized by a marked increase. This means that to understand the experimental manipulation of this sub-process we must consider that naturally the baseline mean slope is not zero but strong and positive (0.55). The ‘High Flat’

and ‘Low Flat’ groups obtained a positive mean slope estimate, whereas the ‘Decrease’ group obtained a non-significant mean slope. Thus, while at first glance it may appear that in absolute terms the manipulation of the use of Selection failed, we can say that in relative terms the manipulation was successful. Indeed, the ‘High Flat’ and ‘Low Flat’ had a smaller mean slope than the ‘Increase’ group (Δχ²=12.81, df=1, p<.001 and Δχ²=7.26, df=1, p=.007, respectively). Similarly, the ‘Decrease’ group had a zero slope, which was also significantly

different from the slope of both the ‘High Flat’ group (Δχ²=9.48, df=1, p=.002) and ‘Low Flat’ group (Δχ²=30.65, df=1, p<.0001). Last, the mean intercept of the ‘High Flat’ and ‘Low Flat’ groups were also significantly different (Δχ²=4.51, df=1, p=.034).