I have no idea about the SPSS and I am having a questionnaire for my dissertation and should analyze it in the SPSS. I downloaded the software and I want to learn the SPSS and how to analyze the questionnaire.
The questionnaire is mixed with Yes/No questions and other staff where are 4 options are available and the sample size is 12.
Can you please instruct me to start with the SPSS and how to analyze the questionnaire.
First of all you have to create a data set. You can either add variables to SPSS data table or import it from Excel. You should have first table row with the name of variables. Every next row would be your case. E.g.:
Id | Var1 | Var2 | Var3 |
01 | 1 | 0 | 1 |
02 | 01 | 03 | 04 |
...
12 | 16 | 12 | 31 |
Second, you have to click "Variable view" and choose the type of variable for every your variable. If you have yes/no variable - you might choose "Nominal". If you have 1 - 4 Variable, it is harder: You have to choose:
"Nominal", if the answers are not comperable or measurable.
"Ordinal", if answers are comperable, i.e. you can say, that answer
1 is lower/higher than answer 2 or 3 or 4, but you cannot say
how much higher/lower.
"Scale", If answers are measurable, i.e. you can say, that answer 2 is twice the answer 1, and answer 4 is answer 1 multiplied by 4. This could be the case if you use Likert Scales (Answers: 1 - "Do not agree", 4 - "Completey agree", 2 and 3 - something inbetween).
N.B.: There are some researchers who would say LIkert Scale is
rather "ordinal" than "scale".
Third, you have to find the correct method. It depends on you research question. There are a lot of methods and, unfortunately, we need to write a book about what method to use. I think, if you write some words about your hypothesis, research question and data, somebody can answer your question.
I hope it helped.
Best,
Eugene
Here's some information on calculating scale scores in SPSS
Here's some general information about basic steps for analysing questionnaire data in SPSS.
In general, you might want to get a copy of the SPSS Survival Manual. It is particularly suited to people getting started with SPSS for thesis analysis.
Related
On the Wikipedia page of Finite State Machines it shows a graphic of the automata types:
I've never heard of combinational logic being included in the automata theory, normally just the Chomsky hierarchy, which stars with FSM. How then would combinational logic be written using a state machine?
For example, if we have an AND gate, I'd see it in a circuit diagram as something like:
______
A ------- | |
| AND |------- C
B ------- |______|
And the states would be: 1(A) & 1(B) --> 1(C), 1&0->0, 0&1->0, 0&0->0. But this involves two initial states rather than one, and also the input to a 'gate' is the combination of two inputs rather than one, so how would this be shown using a FSM? I suppose it could be possible doing something like the following -- with the input symbols being {0,1} and the output {0,1} like a Moore machine.
1 1
s0 ----> s2 -----> s3:1
| | 0
------> s3:0 --0,1--|
0 ^----------|
But this seems a bit useless to me so maybe I'm getting it wrong, what then would be a proper way to model Combinational logic in a state diagram?
Here would be a simpler way to diagram the above, where the Input and Output states are either ON (1) or OFF (0) to make it more intuitive.
I have 2 variables, one for raw p-values and another for adjusted p-values. I need to compute a new variable based on the values of these two variables. What I need to do isn't too complicated, but I have a hard time doing it in SPSS because I can't figure out how I can reference a particular row for an existing variable in SPSS syntax.
The first column lists raw p-values in ascending order. The next column lists adjusted p-values, but these adjusted p-values are still incomplete. I need to compare two adjacent p-values in the adjusted p-values column (e.g., row 1 and 2, row 2 and 3, row 3 and 4, and so forth), and take the p-values whichever is smaller in each of these comparisons and enter those p-values into the following column as values for a new variable.
However, that's not the end of the story. One more condition has to be met. That is, the new p-values have to be in the same order as the raw p-values. However, I cannot ensure this if I start the comparisons from the top row. You can see that (i') is greater than (h') and (g'), and (d') is greater than (c'), (b'), and (a') in the example below (picture).
In order to solve this issue, I would need to start the comparison of the adjusted p-values from the bottom. In addition, I would need to compare the adjusted p-values to the new p-values of one row below. One exception is that I can simply use the value of (a) as the value of (a') since the value of (a) should always be the greatest of all the p-values as a rule. Then, for (b') , I need to compare (b) and (a') and enter whichever is smaller as (b'). For (c'), I need to compare (c) and (b') and enter whichever is smaller as (c'), and so forth. By doing this way, (d') would be 0.911 and (i') would be 0.017.
Sorry for this long post, but I would really appreciate if I can get some help to do this task in SPSS.
Thank you in advance for your help.
Raw p-values | Adjusted p-values (Temporal)| New p-values (Final)
-------------|-----------------------------|---------------------
0.002 | 0.030 (i) | 0.025 (i')
0.003 | 0.025 (h) | 0.017 (h')
0.004 | 0.017 (g) | 0.017 (g')
0.005 | 0.028 (f) | 0.028 (f')
0.023 | 0.068 (e) | 0.068 (e')
0.450 | 1.061 (d) | 1.061 (d')
0.544 | 1.145 (c) | 0.911 (c')
0.850 | 0.911 (b) | 0.911 (b')
0.974 | 0.974 (a) | 0.974 (a')
Another tool that may be convenient is the SHIFT VALUES command. It can move one or more columns of data either forward or backward.
I wonder whether the purpose of this has to do with adjusting p values for multiple testing corrections as with Benjamin-Hochberg FDR or others similar. If that is the case, you might find the STATS PADJUST (Analyze > Descriptives > Calculate adjusted p values) extension command useful. It offers six adjustment methods. You can install it from the Utilities (pre-V24) or Extensions (V24+) menu.
To get you started, here are a few tools that can help you with this task:
The LAG function
you can compare values in this line and the previous one, for example, the following will compare the Pval in each line to the one in the previous one, and put the smaller of the two in the NewPval:
compute NewPVal=min(Pval, lag(Pval)).
If you want to do the same process only start from the bottom, you can easily sort your data in reverse order and do the same.
CREATE + LEAD
if you want to make comparisons to the next line instead of the previous line, you should first create a "lead" variable and then compare to it.
for example, the following syntax will create a new variable that for each line contains the value of Pval in the next line, and then chooses the smaller of the two for the NewPval:
create /LeadPval=LEAD(Pval 1).
compute NewPVal=min(Pval, LeadPval).
Using case numbers
You can use case numbers (line numbers) in calculations and in conditions. For example, the following syntax will let you make different calculations in the first line and the following ones:
if $casenum=1 NewPval=Pval.
if $casenum>1 NewPVal=min(Pval, lag(Pval)).
My dataset includes TWO main variables X and Y.
Variable X represents distinct codes (e.g. 001X01, 001X02, etc) for multiple computer items with different brands.
Variable Y represents the tax charged for each code of variable X (e.g. 15 = 15% for 001X01) at a store.
I've created categories for these computer items using dummy variables (e.g. HD dummy variable for Hard-Drives, takes value of 1 when variable X represents a HD, etc). I have a list of over 40 variables (two of them representing X and Y, and the rest is a bunch of dummy variables for the different categories I've created for computer items).
I would like to display the averages of all these categories using a loop in Stata, but I'm not sure how to do this.
For example the code:
mean Y if HD == 1
Mean estimation Number of obs = 5
--------------------------------------------------------------
| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
Tax | 7.1 2.537716 1.154172 15.24583
gives me the mean Tax for the category representing Hard Drives. How can I use a loop in Stata to automatically display all the mean Taxes charged for each category? I would do it by hand without a problem, but I want to repeat this process for multiple years, so I would like to use a loop for each year in order to come up with this output.
My goal is to create a separate Excel file with each of the computer categories I've created (38 total) and the average tax for each category by year.
Why bother with the loop and creating the indicator variables? If I understand correctly, your initial dataset allows the use of a simple collapse:
clear all
set more off
input ///
code tax str10 categ
1 0.15 "hd"
2 0.25 "pend"
3 0.23 "mouse"
4 0.29 "pend"
5 0.16 "pend"
6 0.50 "hd"
7 0.54 "monitor"
8 0.22 "monitor"
9 0.21 "mouse"
10 0.76 "mouse"
end
list
collapse (mean) tax, by(categ)
list
To take to Excel you can try export excel or put excel.
Run help collapse and help export for details.
Edit
Because you insist, below is an example that gives the same result using loops.
I assume the same data input as before. Some testing using this example database
with expand 1000000, shows that speed is virtually the same. But almost surely,
you (including your future you) and your readers will prefer collapse.
It is much clearer, cleaner and concise. It is even prettier.
levelsof categ, local(parts)
gen mtax = .
quietly {
foreach part of local parts {
summarize tax if categ == "`part'", meanonly
replace mtax = r(mean) if categ == "`part'"
}
}
bysort categ: keep if _n == 1
keep categ mtax
Stata has features that make it quite different from other languages. Once you
start getting a hold of it, you will find that many things done with loops elsewhere,
can be made loop-less in Stata. In many cases, the latter style will be preferred.
See corresponding help files using help <command> and if you are not familiarized with saved results (e.g. r(mean)), type help return.
A supplement to Roberto's excellent answer: After collapse, you will need a loop to export the results to excel.
levelsof categ, local(levels)
foreach x of local levels {
export excel `x', replace
}
I prefer to use numerical codes for variables such as your category variable. I then assign them value labels. Here's a version of Roberto's code which does this and which, for closer correspondence to your problem, adds a "year" variable
input code tax categ year
1 0.15 1 1999
2 0.25 2 2000
3 0.23 3 2013
4 0.29 1 2010
5 0.16 2 2000
6 0.50 1 2011
7 0.54 4 2000
8 0.22 4 2003
9 0.21 3 2004
10 0.76 3 2005
end
#delim ;
label define catl
1 hd
2 pend
3 mouse
4 monitor
;
#delim cr
label values categ catl
collapse (mean) tax, by(categ year)
levelsof categ, local(levels)
foreach x of local levels {
export excel `:label (categ) `x'', replace
}
The #delim ; command makes it possible to easily list each code on a separate line. The"label" function in the export statement is an extended macro function to insert a value label into the file name.
I have to partition a multiset into two sets who sums are equal. For example, given the multiset:
1 3 5 1 3 -1 2 0
I would output the two sets:
1) 1 3 3
2) 5 -1 2 1 0
both of which sum to 7.
I need to do this using Z3 (smt2 input format) and "Linear Arithmetic Logic", which is defined as:
formula : formula /\ formula | (formula) | atom
atom : sum op sum
op : = | <= | <
sum : term | sum + term
term : identifier | constant | constant identifier
I honestly don't know where to begin with this and any advice at all would be appreciated.
Regards.
Here is an idea:
1- Create a 0-1 integer variable c_i for each element. The idea is c_i is zero if element is in the first set, and 1 if it is in the second set. You can accomplish that by saying that 0 <= c_i and c_i <= 1.
2- The sum of the elements in the first set can be written as 1*(1 - c_1) + 3*(1 - c_2) + ... +
3- The sum of the elements in the second set can be written as 1*c1 + 3*c2 + ...
While SMT-Lib2 is quite expressive, it's not the easiest language to program in. Unless you have a hard requirement that you have to code directly in SMTLib2, I'd recommend looking into other languages that have higher-level bindings to SMT solvers. For instance, both Haskell and Scala have libraries that allow you to script SMT solvers at a much higher level. Here's how to solve your problem using the Haskell, for instance: https://gist.github.com/1701881.
The idea is that these libraries allow you to code at a much higher level, and then perform the necessary translation and querying of the SMT solver for you behind the scenes. (If you really need to get your hands onto the SMTLib encoding of your problem, you can use these libraries as well, as they typically come with the necessary API to dump the SMTLib they generate before querying the solver.)
While these libraries may not offer everything that Z3 gives you access to via SMTLib, they are much easier to use for most practical problems of interest.
I am coding a survey that outputs a .csv file. Within this csv I have some entries that are space delimited, which represent multi-select questions (e.g. questions with more than one response). In the end I want to parse these space delimited entries into their own columns and create headers for them so i know where they came from.
For example I may start with this (note that the multiselect columns have an _M after them):
Q1, Q2_M, Q3, Q4_M
6, 1 2 88, 3, 3 5 99
6, , 3, 1 2
and I want to go to this:
Q1, Q2_M_1, Q2_M_2, Q2_M_88, Q3, Q4_M_1, Q4_M_2, Q4_M_3, Q4_M_5, Q4_M_99
6, 1, 1, 1, 3, 0, 0, 1, 1, 1
6,,,,3,1,1,0,0,0
I imagine this is a relatively common issue to deal with but I have not been able to find it in the R section. Any ideas how to do this in R after importing the .csv ? My general thoughts (which often lead to inefficient programs) are that I can:
(1) pull column numbers that have the special suffix with grep()
(2) loop through (or use an apply) each of the entries in these columns and determine the levels of responses and then create columns accordingly
(3) loop through (or use an apply) and place indicators in appropriate columns to indicate presence of selection
I appreciate any help and please let me know if this is not clear.
I agree with ran2 and aL3Xa that you probably want to change the format of your data to have a different column for each possible reponse. However, if you munging your dataset to a better format proves problematic, it is possible to do what you asked.
process_multichoice <- function(x) lapply(strsplit(x, " "), as.numeric)
q2 <- c("1 2 3 NA 4", "2 5")
processed_q2 <- process_multichoice(q2)
[[1]]
[1] 1 2 3 NA 4
[[2]]
[1] 2 5
The reason different columns for different responses are suggested is because it is still quite unpleasant trying to retrieve any statistics from the data in this form. Although you can do things like
# Number of reponses given
sapply(processed_q2, length)
#Frequency of each response
table(unlist(processed_q2), useNA = "ifany")
EDIT: One more piece of advice. Keep the code that processes your data separate from the code that analyses it. If you create any graphs, keep the code for creating them separate again. I've been down the road of mixing things together, and it isn't pretty. (Especially when you come back to the code six months later.)
I am not entirely sure what you trying to do respectively what your reasons are for coding like this. Thus my advice is more general – so just feel to clarify and I will try to give a more concrete response.
1) I say that you are coding the survey on your own, which is great because it means you have influence on your .csv file. I would NEVER use different kinds of separation in the same .csv file. Just do the naming from the very beginning, just like you suggested in the second block.
Otherwise you might geht into trouble with checkboxes for example. Let's say someone checks 3 out of 5 possible answers, the next only checks 1 (i.e. "don't know") . Now it will be much harder to create a spreadsheet (data.frame) type of results view as opposed to having an empty field (which turns out to be an NA in R) that only needs to be recoded.
2) Another important question is whether you intend to do a panel survey(i.e longitudinal study asking the same participants over and over again) . That (among many others) would be a good reason to think about saving your data to a MySQL database instead of .csv . RMySQL can connect directly to the database and access its tables and more important its VIEWS.
Views really help with survey data since you can rearrange the data in different views, conditional on many different needs.
3) Besides all the personal / opinion and experience, here's some (less biased) literature to get started:
Complex Surveys: A Guide to Analysis Using R (Wiley Series in Survey Methodology
The book is comparatively simple and leaves out panel surveys but gives a lot of R Code and examples which should be a practical start.
To prevent re-inventing the wheel you might want to check LimeSurvey, a pretty decent (not speaking of the templates :) ) tool for survey conductors. Besides I TYPO3 CMS extensions pbsurvey and ke_questionnaire (should) work well too (only tested pbsurvey).
Multiple choice items should always be coded as separate variables. That is, if you have 5 alternatives and multiple choice, you should code them as i1, i2, i3, i4, i5, i.e. each one is a binary variable (0-1). I see that you have values 3 5 99 for Q4_M variable in the first example. Does that mean that you have 99 alternatives in an item? Ouch...
First you should go on and create separate variables for each alternative in a multiple choice item. That is, do:
# note that I follow your example with Q4_M variable
dtf_ins <- as.data.frame(matrix(0, nrow = nrow(<initial dataframe>), ncol = 99))
# name vars appropriately
names(dtf_ins) <- paste("Q4_M_", 1:99, sep = "")
now you have a data.frame with 0s, so what you need to do is to get 1s in an appropriate position (this is a bit cumbersome), a function will do the job...
# first you gotta change spaces to commas and convert character variable to a numeric one
y <- paste("c(", gsub(" ", ", ", x), ")", sep = "")
z <- eval(parse(text = y))
# now you assing 1 according to indexes in z variable
dtf_ins[1, z] <- 1
And that's pretty much it... basically, you would like to reconsider creating a data.frame with _M variables, so you can write a function that does this insertion automatically. Avoid for loops!
Or, even better, create a matrix with logicals, and just do dtf[m] <- 1, where dtf is your multiple-choice data.frame, and m is matrix with logicals.
I would like to help you more on this one, but I'm recuperating after a looong night! =) Hope that I've helped a bit! =)
Thanks for all the responses. I agree with most of you that this format is kind of silly but it is what I have to work with (survey is coded and going into use next week). This is what I came up with from all the responses. I am sure this is not the most elegant or efficient way to do it but I think it should work.
colnums <- grep("_M",colnames(dat))
responses <- nrow(dat)
for (i in colnums) {
vec <- as.vector(dat[,i]) #turn into vector
b <- lapply(strsplit(vec," "),as.numeric) #split up and turn into numeric
c <- sort(unique(unlist(b))) #which values were used
newcolnames <- paste(colnames(dat[i]),"_",c,sep="") #column names
e <- matrix(nrow=responses,ncol=length(c)) #create new matrix for indicators
colnames(e) <- newcolnames
#next loop looks for responses and puts indicators in the correct places
for (i in 1:responses) {
e[i,] <- ifelse(c %in% b[[i]],1,0)
}
dat <- cbind(dat,e)
}
Suggestions for improvement are welcome.