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Using SAS for Data Management, Statistical Analysis, and Graphics
Quick and Easy Access to Key Elements of Documentation
Includes worked examples across a wide variety of applications, tasks, and graphics A unique companion for statistical coders, Using SAS for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in SAS, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. Organized by short, clear descriptive entries, the book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, multivariate methods, and the creation of graphics. Through the extensive indexing, cross-referencing, and worked examples in this text, users can directly find and implement the material they need. The text includes convenient indices organized by topic and SAS syntax. Demonstrating the SAS code in action and facilitating exploration, the authors present example analyses that employ a single data set from the HELP study. They also provide several case studies of more complex applications. Data sets and code are available for download on the book’s website. Helping to improve your analytical skills, this book lucidly summarizes the features of SAS most often used by statistical analysts. New users of SAS will find the simple approach easy to understand while more expert SAS programmers will appreciate the invaluable source of task-oriented information.
Includes worked examples across a wide variety of applications, tasks, and graphics A unique companion for statistical coders, Using SAS for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in SAS, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. Organized by short, clear descriptive entries, the book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, multivariate methods, and the creation of graphics. Through the extensive indexing, cross-referencing, and worked examples in this text, users can directly find and implement the material they need. The text includes convenient indices organized by topic and SAS syntax. Demonstrating the SAS code in action and facilitating exploration, the authors present example analyses that employ a single data set from the HELP study. They also provide several case studies of more complex applications. Data sets and code are available for download on the book’s website. Helping to improve your analytical skills, this book lucidly summarizes the features of SAS most often used by statistical analysts. New users of SAS will find the simple approach easy to understand while more expert SAS programmers will appreciate the invaluable source of task-oriented information.
306 pages, Paperback
First published July 16, 2010
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April 18, 2017
I'm learning SAS this semester in school after already having learned SPSS and R in previous years. My professor failed to suggest a book for supplemental learning of SAS and so I purchased this one because I already own the book that these authors wrote for learning R and had loved it. The R book was clearly written, well organized, easy to follow, and even had information on statistics included rather than only the use of the software program.
When I received this book, I was disappointed in how difficult to read and poorly organized the initial chapters seemed to be. I can't fault the authors entirely as every book and website I have read concerning learning SAS has been just as bad. It's almost as if SAS told people if they wanted to write 'how to' sources on the program that they had to do so in a ridiculously convoluted way... possibly even required that they all had to do it in the same ridiculously convoluted way.
It took me 30+ minutes just to figure out how to import my data into SAS and call it because the book starts with how to manipulate data - you know, the data you don't have in the system yet because you haven't imported your data yet. When it does get around to telling you how to import, the book tells you about the different ways to import data but isn't clear on how you call the data set after you import it. Is the data automatically ready for use or do you have to save it? Do you call data differently if it is a data set imported to a library vs. manual entry vs. table entry? If you use two data sources at once are they both available at all times or do you have to switch between sets? When you say 'use the automated import function', where and how do you access that function and what are the steps involved to be sure I haven't missed anything important in the process (settings, etc.)?
These were just the initial issues I had with the book. It does seem to be thorough and have quite a bit of information in it, so I think the book could be useful for someone who has a small amount of cursory knowledge in using SAS to get them started. However, for a complete novice who isn't familiar with the language of SAS or the basics, the book can be frustrating and difficult to navigate due to poor choice in organization / ordering of topics.
Again, the R volume was not this difficult to navigate in my opinion and so my expectations were high with this book. But then, R also runs on Mac computers, is generally easier to use with drag and drop capabilities and intuitive function naming, and was taught in class more thoroughly by the professor so I also didn't have the difficulty getting started with the program as I did with SAS.
When I received this book, I was disappointed in how difficult to read and poorly organized the initial chapters seemed to be. I can't fault the authors entirely as every book and website I have read concerning learning SAS has been just as bad. It's almost as if SAS told people if they wanted to write 'how to' sources on the program that they had to do so in a ridiculously convoluted way... possibly even required that they all had to do it in the same ridiculously convoluted way.
It took me 30+ minutes just to figure out how to import my data into SAS and call it because the book starts with how to manipulate data - you know, the data you don't have in the system yet because you haven't imported your data yet. When it does get around to telling you how to import, the book tells you about the different ways to import data but isn't clear on how you call the data set after you import it. Is the data automatically ready for use or do you have to save it? Do you call data differently if it is a data set imported to a library vs. manual entry vs. table entry? If you use two data sources at once are they both available at all times or do you have to switch between sets? When you say 'use the automated import function', where and how do you access that function and what are the steps involved to be sure I haven't missed anything important in the process (settings, etc.)?
These were just the initial issues I had with the book. It does seem to be thorough and have quite a bit of information in it, so I think the book could be useful for someone who has a small amount of cursory knowledge in using SAS to get them started. However, for a complete novice who isn't familiar with the language of SAS or the basics, the book can be frustrating and difficult to navigate due to poor choice in organization / ordering of topics.
Again, the R volume was not this difficult to navigate in my opinion and so my expectations were high with this book. But then, R also runs on Mac computers, is generally easier to use with drag and drop capabilities and intuitive function naming, and was taught in class more thoroughly by the professor so I also didn't have the difficulty getting started with the program as I did with SAS.
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