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**Using and Interpreting Statistics: A Practical Text for the Behavioral, Social, and Health Sciences**[] 2020-2-2 19:21

Does the job for my statistics class and came in fairly amazing condition. This has definitely helped me save and is very simple to obtain access to as well as returning it in the end.

**A Guide to Doing Statistics in Second Language Research Using SPSS and R (Second Language Acquisition Research Series)**[] 2020-1-13 18:56

I just wanted to post the associated www service for this book because when I searched for it today it was hard to search and the www service for the first edition was the only one that popped up (sorry, I had to place the stars in!). The www service has extra content, including chapters that were in the first edition but have been chop from the second and are found under "Supplemental Material" (on chi-square, ANCOVA and traditional non-parametric statistics), and also extra content from some of the main chapters, found under "Chapters" (by the way, the words on the www service don't provide links; you have to the "Menu" button on the upper left-hand side). There are also answers to the app activities and datasets. Here is the site:

This book is simple to follow and makes sense of the data steps without getting you lost. Also helpful to look at the back at the odd answers to clearly see how exactly to set up the work according to how the assignment is asking for it. It has helped me understand a bit more about the use of data and is very simple to understand, not like other books that use complex terms or wording.

Amazing for the frequentist-trained social science graduate student but I would not read this if I were an undergraduate. Really a text for the PhD student with statistics as a minor within their program of study or for the PhD who has taken at least 3 or 4 statistics courses at the PhD level. An understanding of probability is highly recommended before attempting this book, but given that, it is a remarkable resource.

In my opinion, "Doing Bayesian data analysis" by John Kruschke is easily the best available introduction to Bayesian statistics, and I would confidently recommend DBDA as replacement of this book's first half. It's in the second half of "Bayesian statistics for the social sciences", where discussion moves to more specialized topics, that the book comes into its own and adds value. It's not a five-star book - looking at the un-commented code excerpts, I cannot say that the author gave his all to the manuscript; speaking of code, too poor the book does not use BUGS - but one that makes a positive impression.

The conceptual coverage here is very appropriate for social scientists -- the advantages and principles of Bayesian methods -- yet when it gets to true applications, it too often dumps R code on the reader in enormous chunks that are unsuitable for a 's an example: Bayesian factor analysis is an zone of high interest and need among psychologists and other social scientists -- but the chapter on it ends with a 50 page section of R code! I'm not kidding; you can check this in the table of contents. The 50 pages are unbroken code printed in landscape format. This has several problems, of which perhaps the three most salient are (1) such heavy sections of code belong in a manual or technical document, not in a book; (2) hardly anyone would realistically wish to work through 50 pages of code just to implement factor analysis; and (3) it is thus more of a disincentive to using Bayesian methods than an advertisement for them!On the other hand, the examples are focused on the kinds of data and models that social scientists care about, unlike most texts on Bayesian methods. For use in a graduate course where students are expected to implement models in R code, I suspect this book would be very useful. For readers who wish more practical, immediately useful instruction, It will support with the concepts but overall might be more of a dissuasion from Bayesian methods than the author intends.

Somehow not the right level. Considerably more advanced than an intro text, but probably not enough for a professional statisticican. I'm interested in Bayesian inference, and feel comfortable at the primary level of Bayes theorem and it's easy applications. I'd hoped for an intermediate text that would lever me up to an grasp of Bayesian approaches to model fitting and seletion,structure equation models etc. Kaplan covers these subjects but his treatment is uneven; at times he is at the right level, and at others goes method off the deep end. I was hoping for more in the method of examples that took the, occasionally quite technical, equations and applied them to a true globe issue so that I could obtain a better grasp of the arguments. There are only a few visual aids, graphs etc, and those that are there are described in only a cursory way. He does give one fairly detailed example of a study of model comparison as applied to data from a longtitudinal study to assess student reading performance from countries around the world. This could have been really amazing if he had developed and illustrated it the end, my view is that this is another book by an expert who can't forget his expertness enough to teach his topic to someone who doesn't already know most of it. It is too bad, because Kaplan certainly seems to be highly accomplished. His book would have benefited if he'd had it read and critiqued by non-experts before he published it.

Some people are of the opinion that statistics should replace calculus in high school/college curricula because of the importance of analytics in the modern business world. I think a related argument could be created about replacing some of the more esoteric parts of graduate statistics/econometrics with causal inference theory. If that opinion is correct, this would be an perfect reference usal inference theory is necessary because the regression techniques now taught to young social scientists as methods of determining cause and result assume endogeneity when the data often don't help such an assumption. They also impose a linear model on the data that can be similarly inappropriate. The non-parametric techniques discussed by Rubin and Imbens, while having their own assumptions, are applicable to a wider range of bin and Imbens summarize the voluminous literature on propensity score and similar causal inference techniques in a manner that is accessible to someone with a solid background in statistics (both frequentist and Bayesian). I read the book cover to cover and, despite already knowing something about Propensity Score techniques, learned a amazing ey start with randomized experiments then explain how the mathematical models developed for such methods are also applicable to observational studies. They then discuss different methods of using the Propensity Score along with tests of the plausibility of such models and bias limits when some of the assumptions in these models are complaint I have is that the various types of exact matching are barely discussed. Considering the growing importance of techniques like Coarsened Exact Matching, this seems like a significant oversight. In addition, the book includes no exercises making it difficult to use as a textbook without some supplementary l in all, though, this work is a must have for those engaged in Causal Inference either academically or in the business world. Even those not making active use of these techniques might search applications to their empirical work once they understand how to properly use Propensity Score analysis.

This book is written in a rather lengthy, quite unstructured way. There are multiple redundant sections spread over various chapters that show the the same or very related content. The book has the feel that it has been written over a decade, or so, and that in the end all draft chapters were place together without scrutinizing again what is really needed. A few equations are wrong, mostly due to typos as it e choice of subjects represents the author's view on what are desirable techniques for causal analysis. Some necessary subjects are omitted, for example double robust estimation (only briefly mentioned) and the model based approach (which is heavily criticized but not described in detail for the case of inference under unconfoundedness). What I found particularly lacking is a more through acc of the bias and variance of various sum, this cook book provides an introduction and some useful sections for the patient reader, but it does not resolve the field's need for a structured, didactically sound and complete introduction to the topic.

This book does not have a very rigorous approach to causation. The applied parts are lacking as well -- they apply the suggested models in “empirical examples” but that's literally it - they just run the models and read the numbers, no insightful interpretation of anything, no justification and so on. If you wish to read something informal but applied, go to Mostly Harmless Econometrics by Angrist and Pischke. If you wish to read something rigorous, go to Causality by Judea Pearl.

Although this might be a amazing book to study causal inference since there are not a lot of choices for now, it was tediously long in a lot of topics. I [email protected]#$%! were written in a more concise way.

Perfect book. Explained the nuts and bolts of mediation analysis in simple easy terms, so novice statisticians and none statisticians can understand and use this valuable tool in their research. Only problem is that the kindle ver is not allowed on the kindle cloud reader. Which is too poor if you need to access it via the kindle cloud reader instead of the kindle application or reader.

**Social Science Research Design and Statistics: A Practitioner's Guide to Research Methods and SPSS Analysis**[] 2020-7-29 18:26

This is THE BEST statistics resource I have found! I have taken plenty of statistics courses and am working on a doctoral degree, but this is the first resource that I have found that puts statistics in laymans terms. All of the terms are clearly defined, all of the analyses are clearly explained, and vital info that is "assumed" in other texts is clearly spelled out. I have recommended this book to a lot of of my peers who have purchased it and been just as happy as I have. THANK YOU for this invaluable resource!

**Social Science Research Design and Statistics: A Practitioner's Guide to Research Methods and SPSS Analysis**[] 2020-7-29 18:26

Drs. Rovai, Baker, and Ponton have absolutely "knocked it out of the park" with their fresh book "Social Science Research Design and Statistics: A Practitioner's Tutorial to Research Methods and SPSS Analysis". Although the title and the book is much more utilitarian than pizzazz and flash, the book delivers on its' promise of being a practitioner's tutorial to research methods.Having purchased several of these types of books through the course of doctoral research, most of which are very non-user friendly and rather unreadable for the mere mortal (a.k.a.: non-career statistician), this book does not fall into that eir compilation is very user friendly and is most appropriate for both the practicing leader, consultant, HR, or organizational development professional who is in need of developing valid and reliable research designs and is sufficiently 'deep in the weeds' enough for the most seasoned statistical e book is thick and 'meaty', but very simple to navigate and the learning lessons in the back of each chapter support reinforce the concepts whether utilized as a text book or simply a amazing review of the material read for the only complaint about this book is that it was not around when I first began by doctoral program.I would HIGHLY recommend this book if you are in any sort of academic program (undergrad, masters, or doctoral) that uses statistics or statistical analysis in the course of their studies, any practitioner who needs to perform statistical research for their profession, or for any professor who is looking for a amazing text to create the concepts easy and understandable for their students. You simply can't go wrong - Thanks Drs. Rovai, Baker, and Ponton for the amazing work.

**Social Science Research Design and Statistics: A Practitioner's Guide to Research Methods and SPSS Analysis**[] 2020-7-29 18:26

This book was very handy and extremely useful for my course;I received an A in the azon is a very amazing resource for education materials at amazing and any book can be found !

Paul Jose has done a amazing job on explaining statistical mediation and moderation: what they mean and how they are done. He writes in clear, easy-to-understand (for a stats book!) English, with relatively few complicated formulas. As an almost-finished Ph.D student, I really want he had written this book a couple of years ago because it would have created my analysis so much easier to finish. Even if I were not interested in statistical mediation, the explanations of IV/DV vs. predictor/outcome would have been worth the cost of the book!I highly recommend it.

This is probably the best book I've seen on moderation and mediation with especially powerful sections showing how do try for both using regression and other statistical methods. Most books fail to create the mediation and moderator distinction as clear or fail to present how relatively easy it is to try for their presence. In a latest lecture for my doctoral research methods class, I used some information from this book and students found it clear.

Prof. Jose did a very amazing job!Explain the statistical theory of Mediation and Moderation analysis briefly. Give lots of hints and resources for applied researchers.I have read half of the book and recommended it to graduate students or researchers on psychology (BTW, Paul himself is a psychologist) and other social science.

So far, this is the best research and statistics book I've read. Everything is so clearly said and stated that I cannot go wrong.I wonder why authors like to explain things abstractly? This book, however, is very direct and specific. I just love it.

**Social Science Research Design and Statistics: A Practitioner's Guide to Research Methods and IBM SPSS Analysis**[] 2020-1-23 19:13

This is a unbelievable book. If you need assistance with understanding statistics and how to use SPSS...this is the book to have with you. It gives you all the primary info on T-test, Anovas, bivariates etc.

This is a amazing book for a social scientist particularly one like myself. I took a number of stats and research classes while working on my Masters and later my Doctorate and I applied those classes to my dissertation research. However, since then I haven't had that much cause to use anything more than easy correlations and primary descriptive statistics. I'm working on a study now that requires some more advanced knowledge and I've been looking for a resource that I could use to refresh my memory and confirm that I'm applying various techniques correctly. My old stats books are very academic in nature, they're almost too theoretical for me at this point in my career. I have only had this book for a week but it has been very helpful and I know I will continue to use it regularly going forward as a reference tool. I would highly recommend this book for any research analyst or for graduate students working on their theses or dissertations. The explanations for the statistical concepts in this book are clear and concise. They are also very applicable to actual research.

**Interaction Effects in Linear and Generalized Linear Models: Examples and Applications Using Stata (Advanced Quantitative Techniques in the Social Sciences Book 12)**[] 2020-6-25 18:34

Thank GOD I found this book. This book provide at first the primary understanding of interactions and slowly to advanced knowledge for OLS and GLM. It also provide an understanding and a practical tools which is the ICLAC to create researcher easier in analysing the interactions.

As I've progressed through my current PhD studies, I've been longing for a concise explanation of different statistical processes. A lot of of the entries in this book describe exactly what the analysis tools measure. I want the book had been published three years sooner because it would have saved me hours of anxiety. This book will be a valuable addition to your statistics/research design resource collection.

First, some context: I purchased this text for independent learning, not for a class. In addition, I have only used it for the statistics content and typically skim or skip altogether the SAS and IBM SPSS should be clear from my rating and "headline," I would generally recommend this book. As of the time of this review, the book has held up well for the most part; the only exception is that the outside edge of the front cover is peeling back a bit.

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horse riding tales - ride with friends[] 2020-5-19 15:11So i just updated it yesterday and when i got on 2 hours after the detail was all GONE !!!! I have a rare horse the black and white one spotted and then u couldn't see the spots left i wanted to begin over but i just got on lvl 7 and 6 horses already the barn detail and everything so i got on it this morning to see that maybe it was just a glitch but i was 100% WRONG. But other than that i love this android game but just one question why does the wild mode have to like 86 gems or your horse has to be level 100 why just it be i wanted to roleplay with my with other people but i have to be a wild horse so why not create it free. But i have you have my 100 help if u wish to create any changes pls fix this problem.

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Applied Multivariate Statistics for the Social Sciences: Analyses with SAS and IBM’s SPSS, Sixth Edition[] 2019-12-23 19:43Amazing purchase! Access to practical examples and actual datasets to replicate examples makes this text a must for postgrad statistics students!

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Applied Multivariate Statistics for the Social Sciences: Analyses with SAS and IBM’s SPSS, Sixth Edition[] 2019-12-23 19:43This was a text book for my multivariate class, it was suggested by the professor. The book is clear, at the same time it goes deep on the info covering thoroughly multivariate analysis. Plus, the was amazing!

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