By Michael Grant

In the blink of an eye.

Everyone disappears.

Gone.

Everyone apart from the younger. youth. heart schoolers. little toddlers. yet now not a unmarried grownup. No lecturers, no police officers, no medical professionals, no mom and dad. long gone, too, are the telephones, net, and tv. there isn't any solution to get support.

Hunger threatens. Bullies rule. A sinister creature lurks. Animals are mutating. And the teenagers themselves are altering, constructing new talents—unimaginable, risky, lethal powers—that develop more desirable by way of the day.

It's a terrifying new international. facets are being selected and struggle is imminent.

The first in a panoramic saga approximately youth scuffling with one another and their darkest selves, long past is a page-turning mystery that might make you examine the realm in a complete new way.

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Additional resources for Gone (Gone, Book 1)

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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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