By Kasie West
Ideal for enthusiasts of Sliding Doors, Kasie West's riveting novel Pivot Point follows a woman with the facility to work out her power futures.
Addison Coleman's lifestyles is one sizeable "What if?" As a Searcher, a distinct form of clairvoyant, at any time when Addie is confronted with a call, she is ready to check out the longer term and notice either results. So whilst her mom and dad inform her they're getting a divorce and he or she has to choose who she desires to reside with, a seek hasn't ever been extra important.
In one destiny Addie resides along with her mother within the lifestyles she's consistently identified and is being pursued via the preferred man at school. within the different she is the hot lady in class, the place she falls for a lovely, quiet artist. Then Addie unearths herself drawn right into a homicide research, and her destiny takes a darker flip. With quite a bit to lose in both destiny, Addie needs to make a decision which truth she's prepared to pass though . . . and who she's keen to reside with out.
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Additional resources for Pivot Point
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).