By Kathy Reichs, Brendan Reichs

Having Temperance Brennan for a great-aunt comes with a number of perks—including an invitation to the well-known forensic anthropologist's ebook signing at Comic-Con. yet whereas Tory and the Virals are taking within the mythical conference in all its nerdy glory, a invaluable Terminator robotic is stolen from the crowded exhibition corridor. whilst the bot-napper threatens to break it except a hefty ransom is paid, Tory leaps on the likelihood to assist her aunt Tempe with the investigation.

Subtle clues lead the Virals into the entire weirdness that Comic-Con has to offer—strange costumes, ferocious mock battles, and fanboy geekdom past their wildest dreams—but the Terminator turns out to have vanished and not using a hint, and the clock is ticking. Can the Virals establish the thief and find the lacking robotic sooner than it's too late?

In this gripping eSpecial, Tory and the Virals turn out once more that no crime is unsolvable whilst the pack is at the prowl.

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Additional resources for Swipe: A Virals Special from G.P. Putnam's Sons

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Included here is a description of each goodness-of-fit statistic reported by Mplus, together with a complete breakdown of its computation. In Chapter 4, we examine another first-order CFA model, albeit this time, as it relates to the hypothesized structure of a well-established measuring instrument. In contrast to the model tested in Chapter 3, the one tested here exhibits strong evidence of model misspecification. As such, I subsequently walk you through various stages of the post hoc model-fitting process in an attempt to establish a better-fitting model that is not only statistically viable and substantively meaningful, but also addresses the issue of scientific parsimony.

If goodness-of-fit is adequate, the model argues for the plausibility of postulated relations among variables; if it is inadequate, the tenability of such relations is rejected. Several aspects of SEM set it apart from the older generation of multivariate procedures. First, as noted above, it takes a confirmatory, rather than exploratory, approach to the data analysis (although aspects of the latter can be addressed). Furthermore, by demanding that the pattern of intervariable relations be specified a priori, SEM lends itself well to the analysis of data for inferential purposes.

The one-way arrows leading from the ERROR callouts to each of the observed variables indicate the impact of measurement error (random and unique) on the observed variables. Likewise, the single-headed arrow leading from the RESIDUAL callout to the endogenous factor represents the impact of error in the prediction of PEER RELATIONS. , structural) equations. Because (a) regression equations represent the influence of one or more variables on another, and (b) this influence, conventionally in SEM, is symbolized by a single-headed arrow pointing from the variable of influence to the variable of interest, we can think of each equation as summarizing the impact of all relevant variables in the model (observed and unobserved) on one specific variable (observed or unobserved).

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