Componente del estado de ánimo general (CAG) Área que reúne los siguientes componentes:

In order to see whether policy priorities influence the scores of the objectives, two analyses have been performed. Firstly, a t-test was performed to determine whether the weighted scores are higher than the unweighted scores. Secondly, it is tested whether there is a relationship between the relative priority the transport authorities give to policy objectives and the relative scores they get for these objectives.

For the t-test, the null hypothesis H0 is that the unweighted score Suw and weighted score Sw are the same and

the alternative hypothesis Ha is that the weighted score is better: 𝐻0: 𝑆𝑢𝑤− 𝑆𝑤= 0

𝐻𝑎: 𝑆𝑢𝑤− 𝑆𝑤< 0

For an α=0.05, the null hypothesis cannot be rejected and therefore it cannot be definitely said that the weighted scores differ from the unweighted scores on the same objectives.

So comparison of the unweighted and weighted scores suggests that policy is not significantly influencing policy outcomes. Next, a check is performed to see whether a relation can be found between the policy priorities and the scores on the objectives. This is done by taking the relative priority of the lower hierarchy level objectives compared to the other regions.

The hypothesis is that a higher priority will lead to a higher score. In order to put these values against each other, they have to be adjusted. The objective score has to be adjusted for the total score of the region. For example: Two regions have a total score of respectively 0.3 and 0.5 and they both have a score of 0.7 for perceived safety, which they both give the same high priority. It could then be argued that the region with a total score of 0.3 has its high priority for perceived safety better reflected in its score, because score for perceived safety is more distinct from the system average than the region with a total score of 0.5. Since both the score for perceived safety and the total score are standardized to a scale of 0 to 1 for anti-ideal to ideal, the indicators are compared based on the score relative to the total score, by subtracting the total score from the objective score for perceived safety. This relative score lies between -1 and 1.

Similarly, the actual priorities cannot be used for evaluating the policy effects. When a system has a low score relative to other systems, a low priority does not necessarily indicate anything. If other authorities assign an even lower priority to that objective, it still would have been expected for this region to have a relatively higher score. Therefore, instead of actual priority, the priority relative to the other regions is the parameter of interest.

The absolute priority is determined as the weight that has been given to the lower hierarchy level objective multiplied by the higher hierarchy level weight, including a correction for the amount of items in the lower hierarchy level. For example: The performance objective ‘reliability’ has a weight of 30% for Gelderland. Performance has a 25.8% weight. There are four performance indicators, which means if no distinction is made, reliability would have a weight of 25%. There has to be a correction for the amount of indicators, otherwise the absolute priorities for performance indicators would be much smaller than for economic durability, because it is split up in more items. Using this correction means the absolute priority for reliability for Gelderland becomes:

𝑃𝑟𝑖𝑜𝑟𝑖𝑡𝑦𝑎𝑏𝑠 =

0.30

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The relative priority is then calculated by normalizing the absolute priorities. Because of the correction of amount of items, the absolute priority no longer represents the weight of the objective, i.e. in the example the weight of reliability for Gelderland for the total score would be 7.74%, instead of 30.96%.

The relative priority is compared to the relative score. The relative score reflects the way a system scores on an objective compared to how the system scores as a whole. Since all the scores are already normalized and reflect the distance from ideal and anti-ideal boundaries, this can simply be done by subtracting the total score from the objective score. A negative relative score means that on that particular objective the system scores worse than on the total score; a positive score means the system scores better. If there’s a relation between priorities and scores, it is to be expected that as the relative priority is higher, the relative score is also better. Figure 13 is an example of what the expected relation should be. Figure 14 shows the results for every couple of relative weight and score for each of the 9 lower hierarchy objectives for all 11 systems. The figures show the lack of relation in the actual situation.

This analysis also does not provide any indication that there’s a relation between policy priorities and scores. Table 31 shows the correlation values between relative priority and relative score. All the points put together show a significant but weak correlation of 0.2075. Overijssel and West-Brabant have moderate correlations between the relative scores and priorities, but it is only significant for Overijssel.

Table 31 Correlations between relative priorities and scores

Data points Correlation relative priorities

and relative score

Significant Total

99

0.2075

yes

27

0.4729

yes

45

0.1293

no

18

0.0432

no

9

0.4866

no

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59 Author R.P.C.Buysse

Figure 13 Example of the expected comparison of relative score to relative priorities

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