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. He is author of over refereed article and four texts including “Statistics: Description The book presents an introduction to statistical methods for students majoring in social science disciplines. Read, highlight, and take notes, across web, tablet, and phone.
Sign In We’re sorry! He has held visiting positions at Harvard University, Boston University, London School of Economics, and Imperial College and has taught courses or short courses for universities and companies in about 20 countries worldwide.
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By “modern”, I mean that it is model rather than test oriented, that it gives heavy emphasis to confidence intervals and p-values rather atresti using arbitrary levels of significance, and that it eschews computational formulae. Sign Up Already have an access code? It has met those expectations Students in geography, anthropology, journalism, and speech also fonlay sometimes required to take at least one statistics course.
The author is successful in his goal of introducing statistical methods in a style that emphasized their concepts and their application to the social sciences rather than the mathematics and computational details behind them. Teh text contains numerous sample printouts, mainly in the style of SPSS and occasionaly SAS, both in chapter text and homework problems. Since the first edition, the increase in computer power coupled with the continued improvement and accessibility of statistical software has had a major impact on the way social scientists analyze data.
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 statisticak. 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.
The presentation of computationally complex methods, such as regression, emphasizes interpretation of software output rather than the formulas for performing the analysis. No previous knowledge of statistics is assumed, and mathematical background is assumed to be minimal lowest-level high-school algebra.
About the Author s.
To help with this, some notation has been simplified or eliminated. There is a stronger focus on real examples and on the integration of statisical software.
The author uses capital Y only as aocial 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. Datasets and other resources where applicable for this book are available here. Instructors, sign in here to see net price.
The main changes are as follows:. The fourth edition has an even stronger emphasis on concepts and applications, with greater attention to “real data” both in the examples fot exercises. Availability This item is out of print and has been replaced methocs Statistical Methods for the Social Sciences, 5th Edition. Strong emphasis on regression topics. The book presents an introduction to statistical methods for students majoring in social science disciplines. Moreover, a wide variety of regression models such as linear regression, ANOVA, logistic regression are taught in the same format, essentially as special ajd of a generalized linear model.
On the other hand, the text is not a cookbook. Students in geography, anthropology, journalism, and speech also are sometimes required to take at least one statistics course.
Statistical Methods for the Social Sciences, 4th Edition
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. This edition also has an appendix explaining how to apply SPSS and SAS to conduct the methods of each chapter and a website giving links to information about other software.
One way analysis of variance. The main concepts to be understood by students are sampling distribution, confidence interval, p-value, linear regression. Probability, sample data, and sampling distributions. Statistical Methods for the Social Sciences, 3rd Edition.
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. Changes in the Fourth Edition: This includes some new exercises that ask students to use applets located at http: Agrest sequences are commonly required of social science graduate students in sociology, political science, and psychology. The book helps in this Advanced topics such as regression and ANOVA emphasize interpreting output from computer packages rather than complex computing formulas.
Agresti & Finlay, Statistical Methods for the Social Sciences | Pearson
Alan AgrestiBarbara Finlay. Table of Contents 1. If you’re interested in creating a cost-saving package for your students, contact your Pearson rep. Because of this, this book does not cover the traditional shortcut hand-computational formulas and approximations. You have successfully signed out and methds be required to sign back in should you need to download more resources. From inside the book. Statistical Methods for the Social Sciences, 4th Edition.
He has been teaching statistics there for 30 years, including the development of three courses in statistical methods for social science students and three courses in categorical data analysis. No previous knowledge of statistics is assumed, and mathematical background is assumed to be minimal lowest-level high-school algebra.