Download An Introduction to Statistical Concepts by Debbie L. Hahs-Vaughn, Richard G. Lomax PDF

By Debbie L. Hahs-Vaughn, Richard G. Lomax

This accomplished, versatile textual content is utilized in either one- and two-semester classes to check introductory via intermediate information. teachers decide upon the subjects which are just right for his or her path. Its conceptual process is helping scholars extra simply comprehend the options and interpret SPSS and learn effects. Key recommendations are easily said and sometimes reintroduced and with regards to each other for reinforcement. a number of examples show their relevance. This variation beneficial properties extra clarification to extend figuring out of the ideas. in simple terms an important equations are included.

In addition to updating all through, the recent variation features:

  • New co-author, Debbie L. Hahs-Vaughn, the 2007 recipient of the collage of principal Florida's collage of schooling Excellence in Graduate instructing Award.
  • A new bankruptcy on logistic regression types for cutting-edge extra advanced methodologies.
  • More on computing self belief durations and accomplishing energy analyses utilizing G*Power.
  • Many extra SPSS screenshots to aid with knowing how one can navigate SPSS and annotated SPSS output to help within the interpretation of results.
  • Extended sections on the right way to write-up statistical leads to APA format.
  • New studying instruments together with chapter-opening vignettes, outlines, and an inventory of key innovations, many extra examples, tables, and figures, packing containers, and bankruptcy summaries.
  • More tables of assumptions and the results in their violation together with how you can try out them in SPSS.
  • 33% new conceptual, computational, and all new interpretative problems.
  • A web site that includes PowerPoint slides, solutions to the even-numbered difficulties, and try out goods for teachers, and for college kids the bankruptcy outlines, key techniques, and datasets that may be utilized in SPSS and different applications, and more.

Each bankruptcy starts off with an overview, an inventory of key thoughts, and a vignette with regards to these ideas. real looking examples from schooling and the behavioral sciences illustrate these innovations. each one instance examines the approaches and assumptions and gives directions for the way to run SPSS, together with annotated output, and the best way to boost an APA sort write-up. helpful tables of assumptions and the consequences in their violation are integrated, in addition to the way to try out assumptions in SPSS. 'Stop and imagine' bins supply valuable assistance for greater realizing the thoughts. every one bankruptcy comprises computational, conceptual, and interpretive difficulties. the knowledge units utilized in the examples and difficulties are supplied on the internet. solutions to the odd-numbered difficulties are given within the book.

The first 5 chapters evaluate descriptive facts together with methods of representing information graphically, statistical measures, the conventional distribution, and chance and sampling. the rest of the textual content covers inferential information concerning ability, proportions, variances, and correlations, simple and complex research of variance and regression types. issues no longer handled in different texts akin to strong tools, a number of comparability and nonparametric techniques, and complex ANOVA and a number of and logistic regression types also are reviewed.

Intended for one- or two-semester classes in records taught in schooling and/or the behavioral sciences on the graduate and/or complicated undergraduate level, knowledge of statistics isn't really a prerequisite. A rudimentary wisdom of algebra is required.

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Extra info for An Introduction to Statistical Concepts

Sample text

In other words, about 43% of the distribution falls below the score of 400. Let us now consider how percentile ranks are computed. 2) where PR(Pi) indicates that we are looking for the percentile rank PR of the score Pi cf is the cumulative frequency up to but not including the interval containing PR(Pi) (again known as cf below) f is the frequency of the interval containing PR(Pi) LRL is the lower real limit of the interval containing PR(Pi) w is the interval width n is the sample size, and finally we multiply by 100% to place the percentile rank on a scale from 0 to 100 (and also to remind us that the percentile rank is a percentage) As an example, consider computing the percentile rank for the score of 17.

Stem width: Each leaf: Stem and Leaf 0 1 1 2 . . 0 1 case(s) 29 Data Representation stem-and-leaf display looks like a grouped type of histogram on its side. , the tens digit). , the unit’s digit). Note that SPSS has grouped values in increments of five. 0” means that there is one frequency for the score of 10. The fact that there are two values of “1” that occur in that stem indicates that the score of 11 occurred twice. , only one 4 in the stem). , no information is lost in a stem-and-leaf display).

This would correspond to the percentage of the distribution that falls below a score of 17. 00% 100% =  PR(17 ) =    25  25   Conceptually, let us discuss how the equation works. First, we have to determine what interval contains the percentile rank of interest. This is easily done because we already know the score is 17 and we simply look in the interval containing 17. The cf below the 17 interval is 12 and n is 25. Thus, we know that we need to go at least 12/25, or 48%, of the way into the distribution to obtain the desired percentile rank.

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