14 April, 2021
Are you preparing for an interview for a position that uses SPSS? If so, you're in luck! We have collected some of the most common interview questions and answers to help you prepare. Questions about your experience with SPSS, your ability to use the software, and how you would solve certain problems are likely to come up. Be sure to read through these questions and answers so that you feel confident during your interview!
Most frequently asked SPSS Interview Questions
Ans: SPSS is a computer application that gives a measurable investigation of information.
The numerous highlights of SPSS are open by means of pull-down menus or can be modified with a proprietary syntax language.
SPSS datasets dependably have a 2-dimensional table structure where the columns normally speak to cases, (for example, people or family units) and the sections or factors speak to estimations, (for example, age, sex, or family unit wage).
SPSS can peruse and compose information from ASCII text files, different measurement bundles, spreadsheets, and databases.
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Ans: We can include the medication remedy status (yes or no) when instructive mediation. We can figure the proportion of the chances utilizing calculated relapse in SPSS.
Ans: To Convert String Variables to Date Variables the most preferable way is ALTER TYPE.
For those who are not familiar to ALTER TYPE, there are some alternative ways.
Let us see the syntax below, here we create a mini dataset that holds 8 string variables. The below syntax shows the 8 most common date formats. And Next, every string variable is converted to a date variable with the help of ALTER TYPE.
String Variables to Date Variables Syntax:
The Date Format in ALTER TYPE says to SPSS which component is the Day, Which component is the month, which component is the year.
3/3/9 which means 3rd March 2009, when we use EDATE8 (dd-mm-yy).
March 3rd, 2009 when we use ADATE8 (mm-dd-yy) and so on as shown in the below Syntax.
In the first string variable, the year, month, and day might be isolated by a dash (- ), a slice (/), a period (.), or even a mix of these. It doesn't make a difference which one is utilized.
On the off chance that two digits are utilized for a considerable length of time, numbers 0 through 44 are deciphered as years 2000 through 2044. Numbers 45 through 99 are deciphered as 1945 through 1999. For additional on this, see Two-Digit Year in String - Cautionary Note.
ALTER TYPE overwrites existing qualities and isn't reversible. It's generally no enormous issue if things turn out badly here as long as you stick to sound practices, for example, working from the linguistic structure. Moreover, you can utilize SPSS Clone Variables Tool before ALTER TYPE.
The Syntax of the Above Example
Create a mini dataset.
data list free/d1 to d8(8a20). begin data 31-dec-99 31.dec.1999 12/31/99 12-31-1999 31.12.99 31/12/1999 99-12-31 1999.12.31 end data.
Convert all strings to dates.
alter type d1(date9). alter type d2(date11). alter type d3(adate8). alter type d4(adate10). alter type d5(edate8). alter type d6(edate10). alter type d7(sdate8). alter type d8(sdate10).
Ans: Note that ALTER TYPE must be utilized for a set number of date designs. Some more intriguing arrangements may require a more adaptable methodology. Second, those on SPSS forms 15 and underneath don't have ALTER TYPE since it was presented in variant 16.
For the two situations, we'll typically separate the year, month, and day by utilizing SUBSTR regularly joined with INDEX and RINDEX. We'll at that point convert these into an SPSS data variable by utilizing the DATE.DMY work. At last, we'll show the number of seconds it holds as a more intelligible date by utilizing FORMATS.
Ans: Create a mini dataset.
data list free/s1 s2(2a20). begin data 1.1.1999 1-jan-99 2.28.1999 2-feb-99 3.31.1999 3-mar-99 4.30.1999 4-apr-99 5.31.1999 5-may-99 6.30.1999 6-jun-99 7.31.1999 7-jul-99 8.31.1999 8-aug-99 9.30.1999 9-sep-99 10.31.1999 10-oct-99 11.30.1999 11-nov-99 12.31.1999 12-dec-99 end data.
Extract day, month, and year from a string.
compute day = number(char.substr(s1,char.index(s1,'.') + 1,char.rindex(s1,'.') - char.index(s1,'.')),f2.0). compute month = number(char.substr(s1,1,char.index(s1,'.') - 1),f2.0). compute year = number(char.substr(s1,char.rindex(s1,'.') +1),f4.0). exe.
Compute date variable.
compute d1 = date.dmy(day,month,year). exe.
Display as a date.
Dealing with Months as Letters
The previous example converted the first string variable but the second is slightly harder. This is because of DATE.DMY requires three numbers but months are now shown as letters (e.g. JAN, FEB, and so on). An easy way to fix this is to REPLACE the months by numbers (00 through 12) using DO REPEAT as shown below. After doing so, one can proceed as in the previous example.
*Replace month letters with month numbers in a string.
do repeat s = 'jan' 'feb' 'mar' 'apr' 'may' 'jun' 'jul' 'aug' 'sep' 'oct' 'nov' 'dec' / n = 1 to 12. compute s2 = replace(s2,s,string(n,n2)). end repeat. exe.
|Related Article: SPSS Tutorials|
Ans: Presently we should assume I'd jump at the chance to increase some knowledge into the rates of male and female respondents. I could initially explore to Analyze Descriptive measurement Frequencies as demonstrated as follows.
I'll now move gender into the variable box and perhaps request a bar chart as well.
Now clicking Ok may seem the obvious thing to do. A much better idea, however, is to click the Paste button. Upon doing so, a new SPSS window opens which is known as the Syntax Editor. It's recognized by the orange icon in its left top corner.
The Syntax Editor contains a FREQUENCIES order which holds the directions we just gave SPSS in the Frequencies exchange. Be that as it may, we don't see the recurrence dispersion and bar graph we requested. This is on account of despite everything we have to run the order we just made.
Ans: SPSS REPLACE replaces a substring in a string with a different (possibly empty) substring.
Ans: URLs are made from "title" by utilizing REPLACE. The Syntax underneath exhibits how to do this.
We have a dataset holding the titles of site pages and we'd get a kick out of the chance to change over these to URLs. For a certain something, we don't care for spaces in URLs. The linguistic structure beneath demonstrates to expel them. Stage 1 makes a minor dataset (simply run and generally overlook it) and stage 3 exhibits how to expel spaces utilizing REPLACE.
Using SPSS software:
In case you are an SPSS customer and you are using SPSS variation 14 or later, you can simply open it as a data record, since SPSS bolsters SAS data archives of different arrangements, for instance, .sas7bdat, .sd7, .sd2, .ssd01, and .xpt. These archives can be perused clearly into SPSS either by methods for using the drawdown menu or by methods for using the sentence structure.
Using the draw-down menus select File - > Open - > Data… and after that for Files of Type select the reason as data record make; by then select the report from the summary and snap Open. That is all to it.
With SPSS language structure we can use the get sas order to peruse in a SAS data record.
get sas data='C:datastates.sas7bdat'.
Using SAS software:
Now and again, there is a prerequisite for changing over a SAS archive to an SPSS record outside of SPSS. For example, your partner is an SPSS customer who uses a more prepared adjustment of SPSS and you are a SAS customer working with SAS variation 9.x. For your partner to use comparative data in SPSS that you have tackled in SAS, you can simply change over your data in SAS to an SPSS data report for your accomplice.
In SAS, we can in like manner save a SAS data record as an SPSS data report using proc convey. For example, we have a SAS educational accumulation called my data in the work index and we can do the going with to change over it to SPSS called newdata3.sav. By demonstrating the archive enlargement as .sav, SAS understands that we require our data record to be changed over to SPSS. Amid the time spent change, SAS will normally change over the variable names and regard names moreover.
proc convey data=mydata outfile= "C:datanewdata3.sav";
There may be circumstances where neither alternatives above would work. For instance, in the event that somebody has a SAS information document, works with a more established adaptation of SPSS and does not approach SAS 9.x. Most likely the least demanding arrangement in this kind of circumstances is to utilize Stat/Transfer.
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