AGRESTI AND FINLAY STATISTICAL METHODS FOR THE SOCIAL SCIENCES PDF

Statistical methods for the social sciences / Alan Agresti, Barbara Finlay Agresti, an introduction to statistical methods for students majoring in social science. Such sequences are commonly required of social science graduate students in sociology, political Alan Agresti, Barbara Finlay The book presents an introduction to statistical methods for students majoring in social science disciplines. APA Citation. Agresti, A., & Finlay, B. (). Statistical methods for the social sciences (Fourth edition, Pearson new international edition.). London: Pearson.

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Chapter 16 includes new sections on longitudinal data analysis and multilevel hierarchical models. Such sequences are commonly required of social science graduate students in sociology, political science, and psychology.

Reliance on an overly simplistic recipe-based approach to statistics is not the route to good statistical practice.

Such sequences are commonly required of social science graduate students in sociology, political science, and psychology. This edition also has an appendix explaining how to apply SPSS and Ssocial to conduct the methods of each chapter and a website giving links to information about other software. Alan AgrestiBarbara Finlay. Websites and online courses.

Table of Contents 1. Although not quite as informative, this will again make results consistent with software output, and help students connect the idea of the se for the various inferential methods they see.

New to This Edition. No previous knowledge of statistics is assumed, and mathematical background is assumed to be minimal lowest-level high-school algebra. It provides good examples with SPSS output. This edition has a somewhat lower technical level in the first nine chapters, to make the book more easily accessible to undergraduate students. The work is protected by local and international copyright laws and is provided solely for the use of instructors in teaching their courses and assessing student learning.

This includes some new exercises that ask students to use applets located at http: One way analysis of variance. To help with this, some notation has been simplified or eliminated. About the Author s. A technically correct presentation. The author, in this new edition, uses the symbol se for estimated standard errors, rather than the notation of sigma-hat with subscript having the estimator symbol. The book contains sufficient material for a two-semester sequence of courses.

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The author uses capital Y only as notation for a variable and lower-case for observed values and sample statistics; thus, y-bar, rather than Y-bar, which is consistent with the lower-case used throughout for the standard deviation and other statistics. If you’re interested in creating a cost-saving package for your students, contact your Pearson rep. The fourth edition has an even stronger emphasis on concepts and applications, with greater attention to “real data” both in the examples and exercises.

Integration of descriptive and inferential statistics from an early point in the text. The book contains sufficient material for a two-semester Username Password Forgot your username or password?

Statistical Methods for the Social Sciences

Emphasis on fpr, rather than computing formulas. Reviews “This text is readable, understandable, and well-organized. The presentation of computationally complex methods, such as regression, emphasizes interpretation of software output statistifal than the formulas for performing the analysis.

Strong emphasis on regression topics. My library Help Advanced Book Search. He has been teaching statistics there for 30 years, including the development of three courses agrewti statistical methods for social science students and three courses in categorical data analysis.

Students in geography, anthropology, journalism, and speech also are sometimes required to take at least one statistics course. Instructor resource file download The work is protected by local and international copyright laws and is provided solely for the use of instructors in teaching their courses and assessing student learning.

Sign Up Already have an access code? Datasets and other resources where applicable for this book are available here. The book contains sufficient material for a two-semester sequence of courses.

Instructors, sign in filay to see net price. The book presents an introduction to statistical methods for students majoring in social science disciplines. This item is out of print and has been replaced with Statistical Methods for the Social Sciences, 5th Edition. On the other hand, the text is not a cookbook.

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No previous knowledge of statistics is assumed, and mathematical background is assumed to be minimal lowest-level high-school algebra. User Review – Flag as inappropriate Perfectly reasonable base text; I think one can get through it significantly faster than two semesters, but provides just the base needed for more advanced work.

There is a stronger focus on real examples and on the integration of statisical software. The author is successful in his goal of introducing statistical methods in a style that emphasized their concepts and their application to the social sciencex rather than the mathematics and computational details behind ffinlay.

Statistical Methods for the Social Sciences, 4th Edition

Probability, sample sciehces, and sampling distributions. He is author of over refereed article and four texts including “Statistics: The main changes are as follows:.

The main concepts to be understood by students are sampling distribution, confidence interval, p-value, linear regression.

Because of this, this book does not metnods the traditional shortcut hand-computational formulas and approximations. The main changes are as follows: Statistical Methods for the Social Sciences, 4th Edition. Pearson offers special pricing when you package your text with other student resources. Moreover, a wide variety of regression models such as linear regression, ANOVA, logistic regression are taught in the same format, essentially as special cases of a generalized linear model.

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