Best self-study books for SAS and AMOS

[BeingThere]

My undergrad program only teaches the basics of SPSS (point and click; not syntax.) I'd like to familiarize myself with SAS and AMOS in this last year of undergrad. Does anyone have suggestions for good books with which to learn SAS and AMOS?

[sdt13]

 

Before jumping into AMOS, I would suggest learning the basics in more in-depth, especially regression if you want to learn SEM/Path analysis. 

 

For SAS, I found the Little SAS Book to be useful when I took a course that covered it (although the course focused only on the introductory level, not really in-depth).

I would also ask why you are interested in these two programs specifically. AMOS is licensed through SPSS, while SAS is a whole different program (and from my experience, programs gravitate toward one program). I'm not sure why you chose SAS, as I have never seen that being used in psych (only cognitive sciences), so I would check to see if other programs that are more commonly used fit your needs or are more marketable (e.g., STATA, MPLUS, R). My knowledge of SAS meant nothing when applying, but my in-depth knowledge of SPSS was considered invaluable (especially using Syntax).

[BeingThere]

Thanks for the input, Sdt13. Yes, making myself more marketable is my motive. I have learned multiple regression over the summer and feel pretty comfortable with much of it. I also got introduced to factor analysis, and with the math behind path analysis with the Li book.

I know I will be using AMOS if I do path analysis. I had heard SAS is used, but that was just from one person. I suppose I should investigate what I/O doctoral programs tend to use the most. Anyone have info on this?

I'm up for doing whatever will be most useful later on. So, if you suggest learning syntax in SPSS, then do you know of a good book for that? Or a good book for AMOS?

[Lisa44201]

http://www.amazon.com/Structural-Equation-Modeling-With-AMOS/dp/0805863737

 

This is a good one for AMOS. Quite readable. The program itself is also very user-friendly.

For SAS, I got a copy of the program, went through the tutorials, then used it exclusively for statistical analysis for a month. Anything I would have done with SPSS, I did with SAS. Google the specific analyses to get the syntax, then just use the program.

[spunky]

i think it does depend on the types of analyses you'll be doing.

 

AMOS can be very (and i mean **VERY**) limited in the types of models you can fit.... but then again this is coming from a quant person and we're known for working around with complicated stuff... sometimes a little too complicated lol

Edited August 22, 2013 by spunky

[Lisa44201]

i think it does depend on the types of analyses you'll be doing.

 

AMOS can be very (and i mean **VERY**) limited in the types of models you can fit.... but then again this is coming from a quant person and we're known for working around with complicated stuff... sometimes a little too complicated lol

Hah! True story. AMOS is a good program to start with, though; once you've taken a class or two on SEM, get a copy of Mplus; you'll never look back....

[spunky]

Hah! True story. AMOS is a good program to start with, though; once you've taken a class or two on SEM, get a copy of Mplus; you'll never look back....

 

i gotta say i'm a R fan so i stick to the lavaan package and if i wanna get fancy i use OpenMX. 

 

Mplus is pretty awesome but since it's becoming the go-to software for people doing SEM it's starting to undergo it's own little "SPSS-ification" process and that sucks big time :-/

 

plus its graphing capabilities are... well... let's just call them "mediocre" at best :-)

[Lisa44201]

i gotta say i'm a R fan so i stick to the lavaan package and if i wanna get fancy i use OpenMX. 

 

Mplus is pretty awesome but since it's becoming the go-to software for people doing SEM it's starting to undergo it's own little "SPSS-ification" process and that sucks big time :-/

 

plus its graphing capabilities are... well... let's just call them "mediocre" at best :-)

R?? Nooooooo......... Had bad times trying to get R to do Bayesian analyses; I still cry a little. MATLAB is kinda fun for matrix algebra, and makes really, really pretty pictures. 

 

Mplus has graphing capabilities! Sort of..

[spunky]

R?? Nooooooo......... Had bad times trying to get R to do Bayesian analyses; I still cry a little. MATLAB is kinda fun for matrix algebra, and makes really, really pretty pictures. 

 

Mplus has graphing capabilities! Sort of..

 

noooo!!!!!! don't miss out on R!!! you're gonna get left out from what the NYTimes is calling the "lingua franca" among statisticians and data analysts:

 

http://www.nytimes.com/2009/01/07/technology/business-computing/07program.html?pagewanted=all&_r=0

 

what was your issue with R and Bayesian statistics??... because my master's  thesis was... sorta both of them. a Gibbs sampler for the polychoric correlation coefficient..... done in R. from scratch, lol.

 

MATLAB i like but it's expensive :-( 

Edited August 23, 2013 by spunky

[Lisa44201]

It didn't freaking work. Buggy, persnickety program. The book we were using had code to go with the examples, but the code didn't actually work in the program. Many hours on Google, many tears.I still have it installed; I hate the idea of being defeated by a computer program, but it's collecting electronic dust at the moment. It is also my benchmark for comparison when I get new statistical programs, as in, "Oh, it's not as bad a R," or, "Wow, that's almost as bad as R."

 

Heh, I've been called worse 

[spunky]

The book we were using had code to go with the examples, but the code didn't actually work in the program.

 

 

i'm assuming this was Kruschke's book? 

[Lisa44201]

How did you know? That's damned uncanny.

[spunky]

How did you know? That's damned uncanny.

 

well... it was just deduction (oh i'm such a Sherlock Holmes ). 

 

 

i've been studying Bayesian Statistics since i was doing my BSc (i did a double major in Statistics and Psychology) so i'm familiar with most of the textbooks on Bayesian Statistics out there (there aren't many). Kruschke's book is probably the *only* one that is geared towards people who do not have a mathematics background, and it is specifically aimed at social scientists. i know your technical training did not come from a Statistics Dept because, if it had, you would be so intrinsically familiar with R that you could even code it in your sleep (as i think i mentioned previously, R's the default in Statisics, Comp Sci, etc.). and when you mentioned "The book we were using had code to go with the examples, but the code didn't actually work in the program" my mind went like this: "which textbook on Bayesian Statistics is geared towards non-Statisticians/non-Mathematicians who are not familiar with R and caters to social scientists?" the answers where either Kruschke's book or Lynch's "Introduction to Applied Bayesian Statistics and Estimation for Social Scientists". but Lynch's book is too technical and has very few well-developed examples in R code, so the only logical conclusion left was Kruschke's 

 

 

now that we have estabilished you used Kruschke's book, i can say a few things. Kruschke makes a very decent effort in trying to make an overly complicated subject approachable to people outside from Stats/Math. the problem is that he tries to pack too much in his book. he barely devotes... what? only the  first 3 chapters to something that even remotely resembles the theory of Bayesian statistics? and then he throws the reader into example-code-example-code mode until the end! honestly, i agree with you. the code in the book is buggy but that's mostly Kruschke's fault (not R's). he creates this expectation that if you were to just copy-paste his code into your R console and run it, it would run. but it doesn't! packages get updated all the time. and... well... his code *IS* buggy! now, if you're already good with R that isn't too much of a problem because you can debug what Kruschke wrote pretty easily. but if you're not already familiar with R, i can only imagine, as you mentioned, the endless of hours annoying boredom googling the cryptic error messages R spits back at you. 

 

 

 

my main beef with Kruschke's book is that instead of trying to teach you the logic behind statistical computing for Bayesian statistics and how to build your own MCMCs, he just goes and says "here's the Bayesian alternative for the t-test. here's how you do it in R. copy-paste this code, change the variables and you're ready to go", which is an approach which simply doesn't work for Bayesian analysis. the problem is, of course, that trying to teach said logic would imply the technical/mathematical details would have to increase and that would probably turn off a lot of people not only form his book but from Bayesian Statistics all together. 

 

 

i guess i would just say maybe you'd be willing to give R another chance, coming from a different starting point? really, once you've mastered it, it'll happen just as with Mplus: you'll never look back. heck, you can even ditch Mplus altogether!

[Lisa44201]

Yeah, I'm taking a Stats class in the Stats department this semester - Nonparametrics, even - so I'll be using R again this semester. My main issue with it is that the coding language was not intuitive - at least, to me. I'm not afraid of code - with the exception of SPSS and AMOS, I've taught myself all the statistical software I use, and all of them are code-driven. At some point, they all have a grammatical structure, and the grammar of R, for lack of a better, doesn't make sense to me. I've stumbled across some how-to websites, which have helped, and TINN-R as a code de-bugger has been a lifesaver.

[spunky]

tinn-R is definitely something good to have around. and i think it's very commendable that you're attempting to dwell more in R to make sure you can get a better hang of it. 

 

which programming environments are you used to? C++ maybe? Java? just wondering here what your programming experience has been like to see if you're more familiar with procedural programming or object-oriented programming in terms of paradigms. usually, the paradigm in which you were trained in (or trained yourself) sort of defines the kind of programming "grammar" (like you said) you're used to. and the weird thing about paradigms is that they don't necessarily overlap so if you're thinking along the lines of the "grammar" of a certain paradigm, you may be completely off when trying to work on a different one. 

 

and i totally agree with you. programming in SPSS is a mystery to me. i know SPSS syntax is a spin-off from like this, really, REALLY old version of lingo but, it doesn't behave like lingo at all so i have no clue what it's doing. that's kind of why i ditched it altogether, heh. 

[Lisa44201]

See, I can do SPSS syntax in my sleep - it's essentially VBA, which is used in every Microsoft program known to man (you can even write Macros for Word, if you're so inclined), and is close enough to the good old days of Basic (Line 10: Do blah blah blah, and so on). I'm not really a programmer, as such, which is probably part of the problem with R - I walked into it figuring it was more syntax, just a different language; it is, on some level, but I still haven't figured out the rules. 

[spunky]

well there you go. VBA (and apparently SPSS syntax by extension) are procedural programming environments. R behaves more as an object-oriented programming environment (although it isn't strictly speaking object-oriented). people here in the Sciences prefer it more because you can jump from it to Python, Java or C++ much more easily because they are all object-oriented programs as well. 

 

what about working in SAS instead of R? SAS is procedural as well. it might be more in line with how you're used to thinking about programming. i mean, there's nothing wrong to try and learn both approaches but i guess it could save you some endless hours of googling, lol. 

[Lisa44201]

I've come to enjoy SAS - been mostly great with the nonparametric stuff I've needed it to do, although I think I may be taking a second look at R sooner rather than later - trying to wrap my mind around a non-parametric GLM, which I can (theoretically) do in R, but nowhere else. I'm at the stage where I'm considering if it's really worth it to try and code this thing....

[spunky]

well, i guess the first couple of things that come to mind is (a) why do you need for it to be non-parametric? (don't get me wrong. non-parametrics are cool but i've sometimes found there's usually an easier, more robust parametric alternative out there) and ( when you said 'non-parametric' GLM... which one of the non-parametric family of methods are we talking about? local smoothing? generalized additive models (GAMs)? lasso regression?, etc... 

[Guest |||]

Sometimes I wonder where the best place to direct people is, who have no experience in anything like R (or SPSS syntax, or anything comparable).  It always seems like the learning curve is very steep for new commers and my best advise is usually more eloquently stated "struggle with it for awhile"

[Lisa44201]

Spunky: GAM. The nature of the data is such that a parametric analysis isn't appropriate. 

 

NicholasCage: I stumbled across 

http://www.gardenersown.co.uk/education/lectures/r/basics.htm

and

http://ww2.coastal.edu/kingw/statistics/R-tutorials/index.html

which have been, by far, the most helpful Intro-To-R sites I've come across. 

 

The way I learned VBA was by recording macros in Excel, then opening the macros & reading the code. That gives an idea of the grammar of the program; specific codes can be found through Google. SPSS is written on the same language (so is AMOS, for that matter, but the list of people who program AMOS through the code instead of the graphical interface is very, very small); it then becomes a matter of finding the exact code for the task at hand, and plugging in the variables.

[spunky]

Spunky: GAM. The nature of the data is such that a parametric analysis isn't appropriate. 

 

 

for a nice, concise introduction to GAMs, you can always read Chpt 12 of Extending the Linear Model with R by Julian Faraway. i've found Faraway's book to be a pretty useful resource to become familiar with more advanced regression-like, linear modelling techniques. it gives you a little bit of theory, then some toy dataset and explains each bit of the theory through examples in R code and how to interpret the output. if this is the first time you've dealt with GAMs, it's probably a  good place to start. in general, the "little red books" (everything from the Texts in Statistical Science collection) are pretty nice refernces to have around. the ones i've worked with use R code but i'm sure there's plenty of ones with SAS code out there. 

[spunky]

Sometimes I wonder where the best place to direct people is, who have no experience in anything like R (or SPSS syntax, or anything comparable).  It always seems like the learning curve is very steep for new commers and my best advise is usually more eloquently stated "struggle with it for awhile"

 

you know, you actually place a very intresting question here and i think you hit the nail right in the head with it. when i started programming back in high school i wasn't very good at it (and, heck, even after these many years i know i have a long way to go to get better) but i think you just need to develop two sets of skills: how to think sequentially and how to think modularly. force yourself to structure your thinking. what do i need to get from A to B? what are the series of actions? and thinking modularly in terms of being able to organise series of lower-level commands into big bundles of computer behaviour. how to do that? well practice.practice.practice and patience.patience.patience. learning how to code is no different from learning another language. there is a syntax and there is a logic to it. but we don't get it unless we use it and use it and use it over and over again.

 

 

 

it's interesting to see how  as society becomes more dependent on technology, more and more peolpe are actually less and less inclined (and less interested) in how this technology works and how to interact with it. i praise initiatives like in code.org where big names in the computer world (or even in the entertainment one like Will I Am from the Black Eyed Peas) try to promote this idea of getting middle schoolers and high schoolers coding as soon as possible. worlds of oportunity open up for you. heck, even my colleagues who do QUALITATIVE (yes i said it! such a dirty word ) research marvel at how fast you can go through coding texts for content anylsis with just a a few computer programming tricks. i have to say it pains me a little bit to see brilliant PhD students and professors who should be using their knowledge and talent to interpret data spend endless hours of boredom doing unnecessary, repetitive tasks such as capturing data on a spreadsheet. 

 

 

 

a good friend of mine to whom i've taught R told me that the best way for her to learn it was to do all her homework assignments and class projects in SPSS and R. yes, she mentioned it took her twice the time as everyone else and it was frustrating as hell... but with every new assignment she bacame faster and savvyer until the point now that she has jumped in the R bandwagon and ditched SPSS altogether. i think sometimes people miss the point that ever since SPSS (and SAS along with it) went licence-exclusive, being familiar with only those software packages hinder their ability to market themselves outside big, well-established academic institutions. what if you start working in a small community college with not enough funding to pay for your SPSS licence? or if maybe you want to help some NGO who's already under some severe budget constraints? or even if some journal editor asks you for some alternate analysis that is not available in the drop-down menus you're used to? no one is born known how to code or how to do statistics... but we can do something about it if we try :-)

 

oh! and UCLA's website for everything-statistics is a life-saver:

 

http://www.ats.ucla.edu/stat/

[Guest |||]

Its worse for me as I'm primarly an advocate and user of MATLAB, but its quite pricey.

 

I really should jump wagons and learn something free.