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the rst, the fourth observation with the second, and so on, we could then calculate the Lag 2 autocorrelation. The lag of an autocorrelation refers to how far in the past the dependency among measurements is examined. In the behavioral sciences, the size of the autocorrelation generally decreases as the lag increases. An exception would be with seasonal or cyclic data, which are relatively common and are discussed in more detail in a later section. The interpretation of the pattern of autocorrelations within a time series provides one diagnostic step of the model identi cation process. The calculation and interpretation of the pattern of the related partial autocorrelations calculated at each lag is employed as a second diagnostic step to aid in the identi cation of the speci c ARIMA model that describes the process underlying the time series. Partial autocorrelations are mathematically complex and are not formally de ned here. They are estimated from a solution of the Yule-Walker equation system, and the interested reader should examine Box, Jenkins, and Reinsel (1994), Glass et al. (1975), or West and Hepworth (1991) for a detailed description. The interpretation of partial autocorrelations is that of a measure of the correlation between speci c lags of the time series values after the correlation at the intervening lags has been partialled out or controlled for. Figure 23.4 illustrates the autocorrelations and partial autocorrelations for the ROD data from Figure 23.1. Time Series Model Identi cation Model identi cation ultimately seeks to determine whether autoregressive terms or moving average terms must be included

reader with insight will note an ambiguity here. We could reverse the conditions, making the 1 state a 0 and the 0 state a 1. We address this issue in 10.

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roommates, dating partners, and spouses (see Watson & Clark, 1991, 1994; Watson, Hubbard, & Wiese, 2000) Selfratings on these scales also show strong convergence with the Big Two personality traits of extraversion and neuroticism; for instance, in a combined sample of 4,457 respondents, the general, trait version of the PANAS Negative Affect scale correlated 58 with neuroticism, whereas the Positive Affect scale correlated 51 with extroversion (Watson et al, 1999) We shall examine the construct validity of these general trait ratings in greater detail shortly It also is noteworthy that Watson (2002) recently created parallel forms of the PANAS scales These new scales also are composed of 10 terms each, none of which overlap with those included in the original scales.

the tragic events of December 26, 2004, when an earthquake measuring 9 on the Richter Scale occurred above the Sunda Trench subduction zone near the Indonesian island of Sumatra, triggering a tsunami which killed more than 156,000 people. Large-scale tectonic events such as this are equalled only by volcanic eruptions as natural agents of destruction. Such mega-geohazards

The parallel form of the Positive Affect scale consists of the terms bold, cheerful, concentrating, con dent, daring, delighted, energetic, fearless, joyful, and lively; the alternate form for the Negative Affect scale includes angry, angry at self, blameworthy, dissatis ed with self, disgusted, disgusted with self, frightened, loathing, scornful, and shaky These parallel versions also show excellent psychometric properties For instance, across 12 large between-subject data sets with a combined sample size of 9,887, the new Positive Affect scale had coef cient alphas ranging from 81 to 89 (Mdn = 88) and the Negative Affect scale had alphas ranging from 85 to 91 (Mdn = 87) Moreover, these new measures are strongly convergent with the original scales Across the 12 data sets, the two Positive Affect scales had convergent correlations ranging from 79 to 87 (Mdn = 86) and the Negative Affect scales had correlations ranging from 82 to .

89 (Mdn = 85) Furthermore, trait versions of these parallel forms showed levels of (a) temporal stability and (b) self-other convergence that were fully as good as the original PANAS scales The development of these parallel forms represents a signi cant advance in the assessment of these higher order dimensions; as noted earlier, by using multiple indicators of a construct, one is able to compute corrected, latent correlations that control for measurement error (see Watson, 2002, for more details) Despite (or perhaps because of) their popularity, however, the PANAS scales have not been immune to criticism Some of these criticisms are terminological and need not concern us here (for discussions, see Watson & Clark, 1997; Watson et al, 1999) We will focus instead on one very understandable concern, namely, the unipolar nature of the scales.

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As we have noted, the PANAS scales (and, indeed, virtually all of the commonly used measures of these dimensions) contain only high-end terms, such as active, interested, enthusiastic (Positive Affect), guilty, irritable, and scared (Negative Affect) The dimensions portrayed in Figure 141, however, clearly are bipolar in nature: Low Positive Affect is de ned.

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