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Rating: 
Summary: You NEED this book if you are working on a dissertation
Review: As social science researchers we want to know three things: 1. Can we detect a relationship? 2. Can we define the strength of the relationship? and 3. Can we cognitively make sense of the relationship? Other authors touch on Statistical Power and leave you hanging. Newton and Rudestam open your eyes to something going on out there in the research world that is very leading edge. They expanded me statistically and brought me through a place I've NEVER been before. I think I've got a total of about 7 hours into it now! We, social scientists love to focus on the Type I error...its like Type I diabetes, an epidemic! Newton and Rudestam (p. 83) say, "A finding of statistical signficance means only that the true population effect is probably NOT zero, but typically it is misunderstood as indicating that the study is of substantive significance. A finding that a study is not statistically significant means that we have insufficient evidence to conclude that an effect is present, but it is often misunderstood to mean that the absence of an effect has been demonstrated." Read that about 7 times until you get it and you can call yourself a statistician! Newton and Rudestam (Chapter 4) go into some really DETAILED explanations of the issues of researchers being enamored with and stopping with simple Type I error analysis. They define a series of studies (p.71) and say, "Sadly, the literature offers copius evidence of how research studies in the social sciences are underpowered for detecting all but very large effects." In order to get to a good statistical power level of .8 or higher you have to have some big samples sometimes. It depends upon the effect size of the population itself which you are modelling. Bottom line is this "One cannot conclude the findings are large or important based on the significance level." (Newton, Rudestam, p.88). What they go on to explain very well is that you have to know the power of your testing, you have to estimate the effect size, you have to know if you sample size gives you the power you need. If you don't, you are one of the social scientists who are apparently so enamored with Type I testing that, "...rejecting a null hypothesis is akin to rejecting the proposition that the moon is made of green cheese." (p. 87) As you can tell, I needed this book for its treatise on Power. But, you will find a great deal of other gems in this book. The authors have made it a "one stop shop" for statistical questions. If you have had a bit more than a basic stat class and need to think through stats for research methods so that you won't be seen as an idiot by your scholarly community, BUT THIS BOOK and dog ear it. I am doing just that with it.
Rating:
Summary: You NEED this book if you are working on a dissertation
Review: As social science researchers we want to know three things: 1. Can we detect a relationship? 2. Can we define the strength of the relationship? and 3. Can we cognitively make sense of the relationship? Other authors touch on Statistical Power and leave you hanging. Newton and Rudestam open your eyes to something going on out there in the research world that is very leading edge. They expanded me statistically and brought me through a place I've NEVER been before. I think I've got a total of about 7 hours into it now! We, social scientists love to focus on the Type I error...its like Type I diabetes, an epidemic! Newton and Rudestam (p. 83) say, "A finding of statistical signficance means only that the true population effect is probably NOT zero, but typically it is misunderstood as indicating that the study is of substantive significance. A finding that a study is not statistically significant means that we have insufficient evidence to conclude that an effect is present, but it is often misunderstood to mean that the absence of an effect has been demonstrated." Read that about 7 times until you get it and you can call yourself a statistician! Newton and Rudestam (Chapter 4) go into some really DETAILED explanations of the issues of researchers being enamored with and stopping with simple Type I error analysis. They define a series of studies (p.71) and say, "Sadly, the literature offers copius evidence of how research studies in the social sciences are underpowered for detecting all but very large effects." In order to get to a good statistical power level of .8 or higher you have to have some big samples sometimes. It depends upon the effect size of the population itself which you are modelling. Bottom line is this "One cannot conclude the findings are large or important based on the significance level." (Newton, Rudestam, p.88). What they go on to explain very well is that you have to know the power of your testing, you have to estimate the effect size, you have to know if you sample size gives you the power you need. If you don't, you are one of the social scientists who are apparently so enamored with Type I testing that, "...rejecting a null hypothesis is akin to rejecting the proposition that the moon is made of green cheese." (p. 87) As you can tell, I needed this book for its treatise on Power. But, you will find a great deal of other gems in this book. The authors have made it a "one stop shop" for statistical questions. If you have had a bit more than a basic stat class and need to think through stats for research methods so that you won't be seen as an idiot by your scholarly community, BUT THIS BOOK and dog ear it. I am doing just that with it.
Rating:
Summary: Good book and very helpful
Review: I had Rae Newton for the graduate methods course(s) at CSU Fullerton (502A and 502B). I have used this book many times to answer all of my questions (of course, when Dr. Newton was not available.) In short, I think this book should be used in all methods/stats classes. It really gets to the point and does not waste too much time with all of the formulas. I think it is meant to be used as a guide for non-math/statistics people who are using statistical packages like SPSS. It makes statistics understandable.
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