Book Description
This classic text on multiple regression is noted for its non-mathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for a solid understanding of the rest of the text.
The third edition features an increased emphasis on graphics and
the use of confidence intervals and effect size measures and an accompanying CD with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT.
Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters. The book is an ideal text for courses on multiple regression and correlational methods.
Customer Reviews:
Second Grad Stats.......2007-05-06
I've adopted this text for my graduate seminar in Multiple Regression. I choose it over other texts for the topics AND because it's focus is on concepts rather than math. Now that we can carry SPSSX in our brief case, there is no need to focus on that computation.
Can't beat it.......2001-04-17
...This book is the source of all you need. It's hard going at times, but so's the subject. The book's 15 years old and remains the best guide to the analysis of correlated data. It's a reference book, one I value as much as a good dictionary. To use it as a text would be misguided unless the instruction was aimed at a sophisticated audience.
Best MRC Book Ever.......2000-03-24
I agree with the previous reviewer that there are times when the exposition in the book gets a bit intense; but c'mon! We're dealing with statistics. You gotta sweat a bit. That's when learning happens. In my opinion the book is extremely clearly written. And although you may have to re-read a few sentences a few times, the basic tools for understanding most every major aspect of MRC is embedded in the text. In sum, this was a great book that I read as a 2nd-year graduate student in psychology. Unlike the first reviewer, I turned to this text when I got confused during the course lectures!
MRC Analysis---good book overall.......1999-12-15
Cohen and Cohen's MRC analysis book is well versed and easy to understand for someone that is familiar with MRC terminology, however, for first year graduate students, the text is very equivocal. The book is lacking ample illustrations of complex problems, leaving students to rely on outside sources. Also, the book uses unfamiliar symbols that do not correspond with other MRC books, which intensifies the confusion level of the students even more.
Overall, the text is a great addition to a statistical library, and this reviewer recommends it, in spite of being a sub-par book for first year graduate students.
Book Description
The fourth edition of STATISTICS FOR SOCIAL DATA ANALYSIS continues to show students how to apply statistical methods to answer research questions in various fields. Throughout the text, the authors underscore the importance of formulating substantive hypotheses before attempting to analyze quantitative data. An important aspect of this text is its realistic, hands-on approach. Actual datasets are used in most examples, helping students understand and appreciate what goes into the research process. The book focuses on the continuous-discrete distinction in considering the level at which a variable is measured. Rather than dwelling on the four conventional levels-of-measurement distinctions, the authors discuss statistics for analyzing continuous and discrete variables separately and in combination.
Customer Reviews:
Statistics for indoctrination, philosophy for real dummies.......2005-05-20
For academic philosophers of science sociology is not a paradigm of successful science. Earlier Bohrnstedt had enforced his ersatz philosophy of social science as editor of the journal Sociological Methods and Research. Now in this book, Statistics for Social Data Analysis, Bohrnstedt, Knoke and Mee attempt to indoctrinate students in this same ersatz philosophy of science.
The authors advocate their version of Haavelmo's "structural-equation" agenda, allege a distinction between unobserved conceptual variables and observable "indicators", and pontificate criteria for identifying causality prior to statistical modeling and empirical testing.
Contrast their views with some basics of contemporary pragmatism, which prevails in professional academic philosophy taught in universities today:
1. Pragmatist definition of "theory": A theory is any universally quantified statement proposed for testing. It is never defined in terms of any particular ontology - such as subjective motivations. Thus there is no philosophical problem of relating sociological theory to empirical model, because the theory is the model and the model is the theory.
2. Pragmatist criterion for criticism: Only empirical criteria may operate in the criticism of theories. Ontological ideas including preconceived claims about causality are never valid criteria. Thus theories/models may not be rejected merely because their equation specifications do not describe motivations, i.e. do not have a mentalistic ontology.
3. Pragmatist thesis of ontological relativity: The empirically tested and currently nonfalsified theory decides ontology including any claims about causality. Thus one does not firstly know causes and then make theories, but rather the empirically tested and nonfalsified theories/models describe the ontologies of their domains including causality.
4. Pragmatist thesis of pluralism: There may be and often are multiple empirically acceptable - i.e. tested and currently nonfalsified - theories/models. Thus they all make acceptably competing or complementary causal claims, so long as they are found to be empirically acceptable - i.e. not falsified.
In her book, History of Econometric Ideas, Mary S. Morgan writes that there are two ways in which econometrics has been used: (1) discovery or theory development and (2) empirical testing. The contemporary pragmatist philosophy of science assigns statistical analysis a fundamental role in theory development as well as in theory testing. Pragmatism thus invites use of data mining and artificial-intelligence computer systems, which can create and test literally billions of hypotheses.
I believe that this book, Statistics for Social Data Analysis, leaves the reader/student ignorant of the true capability of new technologies such as mechanized statistical analysis of social data for discovery, and that its provincial philosophy of science invites a Luddite attitude toward twenty-first century social science research.
Sociologists who are unaware of contemporary academic philosophy of science will likely not find this review helpful. More importantly such sociologists will also therefore be unable to exploit to their - or their students' - advantage the enabling freedom and contributing opportunities offered by the pragmatist philosophy.
For more: Google my book, History of Twentieth-Century Philosophy of Science at my web site philsci for free downloads, and to view my other book reviews at this Amazon site.
Thomas J. Hickey, Econometrician
For students of social sciences.......2003-03-31
This book is a statistics textbook for students of social sciences, not high-end users. I read earlier edition of this book in undergraduate statistics course. In that course, only basics of statistics were instructed. In social sciences, they don't need to know A to Z of statistics for all they have to know is what the function of SPSS or SAS means and what kind of data is needed and how the data would be analyzed in the statistics packages. There is no need to derive the functions in the textbook mathematically as they do in the courses of statistics department. We should understand what the function means, not how it is derived. This book is written in this regard. Unlike orthodox statistics textbook, this book tackles only the meaning of the statistical methods. In doing so, this book illustrates the methods with various field works and SPSS exercises. This is the stance most textbook written for social scientists takes. It seems that this book succeed in achieving the goal. Explanations are succinct and examples are apposite.
But this book is not that useful when you should do real research. Most social sciences articles use more advanced methods than what this book introduces. This book is good enough to beginners, but not so to who would be real researcher. At that point, you should have read more advanced ones already. If not, you couldn't read a piece of article in the common journals.
Book Description
"This is a first-class book dealing with one of the most important areas of current research in applied statistics…the methods described are widely applicable…the standard of exposition is extremely high."
--Short Book Reviews from the International Statistical Institute
"The new chapters (10-14) improve an already excellent resource for research and instruction. Their content expands the coverage of the book to include models for discrete level-1 outcomes, non-nested level-2 units, incomplete data, and measurement error---all vital topics in contemporary social statistics. In the tradition of the first edition, they are clearly written and make good use of interesting substantive examples to illustrate the methods. Advanced graduate students and social researchers will find the expanded edition immediately useful and pertinent to their research."
--TED GERBER, Sociology, University of Arizona
"Chapter 11 was also exciting reading and shows the versatility of the mixed model with the EM algorithm. There was a new revelation on practically every page. I found the exposition to be extremely clear. It was like being led from one treasure room to another, and all of the gems are inherently useful. These are problems that researchers face everyday, and this chapter gives us an excellent alternative to how we have traditionally handled these problems."
--PAUL SWANK, Houston School of Nursing, University of Texas, Houston
Popular in the
First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as:
* An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3
* New section on multivariate growth models in Chapter 6
* A discussion of research synthesis or meta-analysis applications in Chapter 7
* Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators
While the first edition confined its attention to continuously distributed outcomes at level 1, this second edition now includes coverage of an array of outcomes types in Part III:
* New Chapter 10 considers applications of hierarchical models in the case of binary outcomes, counted data, ordered categories, and multinomial outcomes using detailed examples to illustrate each case
* New Chapter 11 on latent variable models, including estimating regressions from missing data, estimating regressions when predictors are measured with error, and embedding item response models within the framework of the HLM model
* New introduction to the logic of Bayesian inference with applications to hierarchical data (Chapter 13)
The authors conclude in Part IV with the statistical theory and computations used throughout the book, including univariate models with normal level-1 errors, multivariate linear models, and hierarchical generalized linear models.
Customer Reviews:
pre-req: mid-level stats experience.......2006-07-12
I had taken a class in HLM before, and I bought this book to refresh myself on the details. It takes a good deal of attention to detail and concentration to really get the full measure from this book, although it's all in there. Despite the authors' best efforts, there is a good bit of stats jargon in the book, so a reader who is not familiar might have some difficulty. If you're at a point where learning HLM is a logical next step, you'll be fine and I recommend this book. However, if your over-eager faculty advisor told you to learn HLM, despite your minimal experience in stats, you're better off enrolling in a class or workshop.
Good but sometimes skipping ahead too fast.......2006-03-09
This book gives a detailed description of the use of an advanced method to deal with nested data sets.
At a general level the constructs and ideas are well written and can be followed reasonably easily.
However the mathematics is often written very dense, which makes reading and understanding complex.
My main problem with the book, is that in many of the examples they provide, the given formula's, and data skip rapidly to the solution. Thus it is often not insightfull at all, how the data led to the numerical outcome (and I and several of my colleagues could not reproduce all of the example outcomes). A more extensive discussion and a more step-by-step construction of the examples would have been helpful there.
So in short: Conceptually this book is fine, but for practical use mathematics are too dense, and examples are too hard to follow
Useful, but need solid background in stats.......2004-06-05
This book describes important advances in statistical analysis of social science data, circa 1992. Much of this data has a natural hierarchical grouping. But traditional statistical methods proved inadequate at coping. The biggest drawback was the failure of the assumption of independence. If at the lowest level, Items I1,...,In are grouped into sets J1,...,Jm, where m
To handle this, Hierarchical Linear Models were developed. The book gives a detailed treatment. A very comprehensive discussion. Including the handling of meta-analysis, where we wish to combine results across different studies. Which then involves using empirical Bayesian estimates. This method has also seen important usage in evaluating medical studies, conducted by different researchers on the same topic.
The book also illustrates the essential development of non-trivial computer programs to perform the gruntwork.
You will need a solid background in statistics to find this book useful. At a minimum, a year of statistics at the undergraduate level.
Book Description
The SPSS 14.0 Guide to Data Analysis is a friendly introduction to both data analysis and SPSS. Easy-to-understand explanations and in-depth content make this guide both an excellent supplement to other statistics texts and a superb primary text for any introductory data analysis course. With the book, you get a jump-start on describing data, testing hypotheses, and examining relationships using SPSS. The goal of this book is to provide an unintimidating introduction to data analysis and to SPSS. This edition focuses on topics that interest today's students-in particular, the role of the Internet in society. It is designed for use with SPSS 14.0, including the Student Version. A data CD is included with this book.
For additional information, go to http://www.norusis.com This site offers a detailed Table of Contents, features, examples included in the book, and a sample chapter for download.
Customer Reviews:
Excellent for learning to do SPSS software and/or to learn/understand statistics.......2007-09-21
I have used this book (previous editions) in teaching a graduate level research methods and statistical software class in the late 1990s. It is the best book available for anyone who needs to use SPSS or who needs to know how to organize data, interpret statistical output and understand the process of quantitative research. I now teach short courses and do statistical consulting for faculty, staff and students at a university. Whenever anyone asks what I recommend if they want to get SPSS and/or statistical thinking, its a no brainer. This book is hands-down the best for either or both of those goals.
Stats made easy.......2007-06-12
The book is written in an easy to understand language. The examples help to recreate the steps explained in the different chapters. I can only recommend this book.
Wrong Item.......2007-01-05
I had to return this since it did not indicate that it was "student version" which limits the number of variables.
teaches statistics and SPSS .......2006-07-11
In the humanities and social sciences, SPSS is probably the most heavily used statistical package. Norusis helps you understand why. Even if you do not have a strong background in statistics. The book teaches both statistics and the use of SPSS to analyse your statistical data.
The text starts off with the most basic material, like showing a simple frequency table. Or displaying it visually using a pie chart or a bar chart. Then, when there are too many values for a bar chart, you can use a histogram, which has bins, each representing a range of values of the independent variable. SPSS has the ability to quickly display in these formats.
Then the text progressively takes you into analysis. Starting with the computation of mean, median and variances. Later, when there are several independent variables, other graphing formats like scatterplots come into play. But the more challenging sections involve testing hypothesis. From these come the use of chi square tests, Student's T-distribution, nonparametric tests and so on.
If you make it through the book, you get an impressive self taught education in statistics and SPSS.
Book Description
Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes:
* a chapter covering power analysis in set correlation and multivariate methods;
* a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and;
* expanded power and sample size tables for multiple regression/correlation.
Customer Reviews:
Definitive - But.......2006-08-18
Absolutely the main text but check it out from the library - you will use it approximately 10-15 times in your research life.
The Definitive Power Analysis Text.......1999-12-03
Cohen does a masterful job of taking the guesswork out of statistical power estimation. This text provides procedural guidelines for determining power for many designs, and can be quite helpful in determining proper sample sizes. Not for the casual reader, but a necessary addition to any serious researchers statistical library.
The classic statistical power reference........1999-06-29
Clearly, a must for every statistical library. This book is considered the authority on power analysis.
Book Description
This popular text provides an accessible guide to the application, interpretation, and pitfalls of structural equation modeling (SEM). Reviewed are fundamental statistical concepts--such as correlation, regressions, data preparation and screening, path analysis, and confirmatory factor analysis--as well as more advanced methods, including the evaluation of nonlinear effects, measurement models and structural regression models, latent growth models, and multilevel SEM. The companion Web page offers data and program syntax files for many of the research examples, electronic overheads that can be downloaded and printed by instructors or students, and links to SEM-related resources.
Customer Reviews:
Excellent introduction to SEM.......2007-01-10
Kline's book provides a very readable introduction to and explanation of structural equation modeling. The book does not include statistical proofs, so it would not serve well as an advanced text. But if you are looking for a book that explains what SEM is and how it fits within the larger framework of inferential statistics, I recommend it.
Book Description
For graduate and upper-level undergraduate courses in Marketing Research and Marketing Data Analysis.
Marketing Research: An Applied Orientation, 5e allows students to actually
experience the interaction between marketing research and marketing decision-making.
Customer Reviews:
A bible for MR.......2007-06-02
An excellent book for those who are new to MR or for those who wants to revisit the concepts. A well organised book with many real time examples and case studies to learn the concepts from.
I consider this book as a "Must have" one for MR field.
Customer Reviews:
SPSS 13 Student version.......2006-11-03
I have tried for years a lot of demo, free and shareware related to statistics, econometric and data analysis software. It was good, tired, and finaly I choose SPSS, because has excelent tools, options and interfase to user. Not PC crashes, no unknown format files. If you no not have enough money, don't waste time and money, use SPSS Student version.
Perfect for small projects!.......2006-07-08
Note: SPSS 13.0 for Windows Student Version cannot open data files containing more than 50 variables or 1,500 cases. Having said that, if you have less than 50 variables and 1,500 cases to deal with then this is the software for you. It came with a (brief) 227-page users guide that was pretty comprehensive all by itself. Order the SPSS 13.0 Guide to Data Analysis to go with it, and you have just about everything you need to fiddle with and learn the basics of statistical analysis.
Excellent for SPSS and statistics students.......2006-03-10
I have used this product in a research methods class and found it to perform quite well. While it may not have all the features of the full SPSS package it is more than adequate and does not require the student to pay the hefty fee of the full version.
Very disappointed.......2006-03-02
Beware - if you buy the student version it is not big enough to run the files necessary that you work on in class. Nowhere did it indicate on the Amazon site that it had limits of file size. Wouldn't one assume that if you are selling a "student" verson, that it will be able to run your school files? Don't buy it if you are a college student and need this software to complete your projects. I wasted $80 plus dollars. Didn't learn of its limitations until AFTER I loaded it, and when it wouldn't work found the limitation statement in the manual. Very disappointed.
If your Statistics Class text uses SPSS and did not include it.......2005-12-18
SPSS Student is a dremendous value: over 75% off the retail version and nearly as powerful. It is limited, but is more than adequate for most cases you will encounter in class.
If you need a more advanced package, get SPSS Career Builder or SPSS Graduate Pack -- they include the full version of SPSS.
Book Description
- The updated and expanded second edition of the internationally bestselling guide to principles and practices for undergraduate business and economics students taking mandatory economics statistics courses. - Features four new sections—on nonparametric tests, the Logit Model, the Probit Model, and causality tests—complete with new models and tests used in financial econometrics, and a new chapter on time series econometrics - Over 100,000 students enrolled annually - Includes numerous examples, completely worked problems, supplementary problems, and two full-length self-examinations
Customer Reviews:
It got me through Econometrics.......2002-01-26
This was an extremely useful book for the understanding of Statistics and Econometrics. Each topic had examples to show how the formulas work. The computer chapter went over the programming in SAS, Excel, and Eviews for the problems in the book. Best of all, the problems had answers. This is a must-have for beginning statistics and econometrics since it starts from scratch, and for theory students in search of an application.
Customer Reviews:
very nicely done!.......2004-06-23
I've just started working with this book, but I'm very impressed. Very clear layout, has lots of examples and exercises (with answers to even numbered ones). Very good about explaining why things are done a certain way, and especially strong in asking questions a typical reader might have. I'm recommending this for my graduate course on research methods for information technology.
Books:
- Basic Business Statistics: Concepts and Applications and CD package (10th Edition)
- Bringing Out the Best in Yourself at Work: How to Use the Enneagram System for Success
- : Business Process Management and the Balanced Scorecard : Focusing Processes on Strategic Drivers
- Business Statistics: First Course and Student CD (4th Edition)
- Business Statistics: First Course and Student CD (4th Edition)
- Contemporary Engineering Economics (3rd Edition)
- Continuous Improvement Tools: A Practical Guide to Achieve Quality Results (Volume 2)
- Data Analysis and Decision Making with Microsoft Excel (with InfoTrac and CD-ROM)
- Data Analysis and Decision Making with Microsoft Excel (with InfoTrac and CD-ROM)
- Design for Six Sigma : A Roadmap for Product Development
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Multiple Regression: Testing and Interpreting Interactions
Design and Analysis: A Researcher's Handbook (4th Edition)
Principles and Practice of Structural Equation Modeling, Second Edition (Methodology In The Social Sciences)
Experimental and Quasi-Experimental Designs for Generalized Causal Inference
Statistical Power Analysis for the Behavioral Sciences (2nd Edition)
