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The legitimacy of using the z-score transformation and its associated probabilities depends on the normality of the distribution of the continuous variable Although it is tempting to conclude that most inferential statistics are robust to violations of normality, that conclusion is not warranted Bradley (1982) reported that statistical inference becomes less robust as distributions depart from normality and rapidly so under many conditions And even with a purely descriptive study, normality of variables (as well as pair-wise linearity and homoscedasticity, discussed in the section titled Multivariate Assumptions ) enhances the analysis, particularly when individual variables are nonnormal to varying degrees and in varying directions Skewness and kurtosis are statistics for assessing the symmetry (skewness) and peakedness (kurtosis) of a distribution.



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11122 Forward Generation of RTs In Section 11121, we mentioned that domain values in the RDM are atomic We also mentioned earlier in this chapter that each relation type (RT) in the ERM is represented in the RDM by adding a new domain (a FK) to a relation that corresponds to one or more domains (PKs) for some relation in the RDM Because of these two characteristics, an FK value for a particular relation instance (row) is capable of capturing only one PK value and therefore each relation instance (row) can only be connected to (at most) one instance (row) for the RTs represented this way.

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A distribution with positive skewness has a few cases with large values that lengthen the right tail; a distribution with negative skewness has a few cases with small values that lengthen the left tail A distribution with positive kurtosis is too peaked (leptokurtic); a distribution with negative kurtosis is too at (platykurtic think atty ) A normal distribution is called mesokurtic Nonnormal distributions have different percentages of cases between various standard deviation units than does the normal distribution, so z-score transformations and inferential tests applied to variables with nonnormal distributions are often misleading Figure 51 shows a normal curve and several that depart from normality In a normal distribution, skewness and kurtosis are zero The standard error of skewness is sskewness = The standard error of kurtosis is skurtosis = 24 N (52) 6 N (51).

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This means that transforming any binary RT from the ERM into the RDM is trivial when at least one side has a maximum cardinality of one (we simply add an FK to the relation on the other side of the relationship) Similarly, when we transform a one-to-one relationship, we can add the FK to the relation corresponding to the ET on either side (since they are both on the one side, putting the FK on the other side means that choosing either relation is valid) However, this is not the case for many-to-many RTs When we transform a many-to-many RT, we need to create a new relation containing a separate FK for each participating role (each connection to an ET) This set of FKs must also be used as the of cial PK for the new relation.

It is as if the statements inside the with action were called from the Timeline of the hole instance. The above code is equivalent to the following:

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This is sometimes done in a CDM using a concept called an associative ET Similarly, if we want to transform an RT with degree higher than two (a nonbinary RT), we must use this approach even if the cardinality is one-to-many or one-to-one In both the manyto-many scenario and the nonbinary scenario, we need to create a new relation and use the FKs as the PK, with each FK connecting to a separate relation representing each participating role For example, recall the On RT we discussed for the CDM shown in Figure 103 in the previous chapter: this is an RT with degree equal to three (3) and therefore a nonbinary RT (it is a ternary RT) Figure 119 shows the relational schema for the new relation representing this RT 11123 Forward Generation of ETs The forward generation of a Strong ET is straightforward.

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Figure 17.1 Revised version of the stress and injury model. Note: The original model had directional arrows only from personality to history of stressors and from coping resources to history of stressors; it had no bidirectional arrow between personality and coping resources. Source: Psychosocial Antecedents of Sport Injury: Review and Critique of the Stress and Injury Model, by J. M. Williams and M. B. Andersen, 1998, Journal of Applied Sport Psychology, 10, pp. 5 25. Reprinted with permission.

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