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and the growth and decay probabilities for adding or subtracting one molecule are and , respectively. The (i ) are proportional to c 1 = c and the (i ) are determined from detailed balance when c = c e . These equations, with extra terms allowing coagulation between clusters, were used by McMurry and Friedlander [54], McMurry [55, 56] and Rao and McMurry [57] to describe collision-controlled formation and growth of aerosols when there is no signi cant nucleation barrier. The macroscopic limit of the equations, when i (or the corresponding radius, R) becomes a continuous variable, was explored by Clement and Wood [58, 59], and the extra real-size diffusion in the aerosol size distribution which occurs with the discrete equations has been examined by Clement et al. [60]. The spreading resulting from the diffusion is only likely to be signi cant when supersaturations are small, but might apply to CCN in atmospheric water clouds. For supersaturations of greater than 10%, it was found that the largest spreading could be only 1 2 nm, so that, outside the nucleation region, it is always justi able to use the continuum equation for the growth of a size distribution, n(R, t): n(R)/ t + (n dR/dt)/ R = Sc (R, t) (3.77)

and lognormal The maximum likelihood method with the ARIMA (5, 0, 0) model speci ed produced very accurate results across all conditions It was generally as good or better than the correct model identi cation The maximum likelihood with the ARIMA (1, 0, 0) model generally produced a very good approximation Violations of the distribution assumptions had no effect Computational Issues Analysis of time series data requires the use of a computer program Fortunately, a large number of programs have become available in the last two decades Harrop and Velicer (1990a, 1990b) evaluated ve programs: BMDP (Dixon, 1985), GENTS (Velicer, Fraser, McDonald, & Harrop, 1986), ITSE (Williams & Gottman, 1982), SAS (SAS Institute, 1984), and TSX (Bower & Glass, 1974; Glass, Bower, & Padia, 1974) Simulated data from 44 different ARIMA models were employed to assess the accuracy of the programs (Harrop & Velicer, 1990b).

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Three programs produced generally satisfactory results (TSX, GENTS, and SAS) One was inaccurate across a wide range of models (ITSE), and one was occasionally inaccurate and occasionally failed to complete the analysis (BMDP) The original ITSE contained incorrect formulas and an amended version of this program, ITSACORR, is available (Crosbie, 1993) The overall evaluation of the computation features and quality of documentation was not very favorable (Harrop & Velicer, 1990a) Some of the programs evaluated in the Harrop and Velicer (1990a, 1990b) studies have been substantially modi ed since inclusion in the study and time series analysis has since been added to widely used statistical packages, such as SPSS Trends (SPSS, Inc, 1988) and SYSTAT (Wilkinson, 1986) SAS remains one of the best programs, with extensive supporting features However, a new comparative evaluation of time series programs is needed because the Harrop and Velicer studies are dated.

This common factor growth model is drawn as a path diagram in Figure 18.14. We can also recast these common factor scores into a latent difference form of f [t]n = f [t 1]n + f [t]n f [t]n = and

Measurement Issues One of the impediments to the widespread use of time series in the behavioral sciences has been the problem associated with obtaining appropriate quantitative measures on a large number of occasions Measures previously employed for time series analysis include physiological and behavioral measures such as blood pressure, the number of cigarettes smoked per day, and the number of standard drinks per day Many of the measures that have been employed in crosssectional research studies are simply not appropriate for time series designs Assessment batteries were often lengthy and.

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