How to enter/analyze likert data My research involved a questionnaire with open-ended questions, multi-answer multiple choice (select all that apply; entered as "1" for circled answers, "0" for non-circled), and likert scale questions. I want to examine whether there are differences in responses to the survey (especially the likert questions) based on a variety of independent variables. Independent variables are things such as: age, ward number, intervention group (3 colour-coded groups - this was a randomized clinical trial), treating physician/dietitian (entered into SPSS as string variables), presence/absence of dysphagia (entered as binary data 1,2)...and many others.
I would like to treat the likert data as ordinal data rather than interval. This is my first time conducting a research study and would appreciate pointers as to how to even begin the analysis. My limited understanding is that non-parametric tests are more suitable for my scenario.   
 A: I started doing analytical work years ago with data from surveys/questionnaires  (analyzing in SPSS), so can completely identify.   
Your question is very broad, so here are some general recommendations:
Starting Point:
Quickly learn how to use your software's scripting / coding editor, and do all of your variable setup and transformation work there. This enables: 


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*Editing and rerunning as you inevitably decide to make changes to your setup 

*Easy re-running of your setup if you add more adata, and 

*Documentation for you and others down the road!


Entering the Data:


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*Invest heavily in setting up your dataset.  This stage is often very manual and tedious, but critical.  Shortcuts here will cause pain down the road.

*Definitely transform your likert data into a ordinal integer scales (you can manually do it by summing up the component variables and scaling each choice by x1, x2, x3, up to xN possible scale choices.    

*For likert data scales, pick one conceptual orientation and stick to it!  E.g., if one variable is scaled  'strongly dislike' <--> 'strongly like' then a diet adherence scale should run 'did not adhere to diet'<----->'adhered to diet', not the other way around.

*Be sure to use your software's functionality for adding variable value labels - they'll help your analysis and output tremendously. 

*Aggregate values for variables like age into broader bands to simplify your analysis output.  You have to use subject-matter expertise and judgment given the study to pick the bands.  But also consider resulting sample counts - a band with 1 person in it isn't very useful.

*Be careful of reverse wording.  It's a good technique from a survey/response validity point of view, but trips up analysis.  Recode the values of such questions to match the conceptual orientation of other likert scales per the point above.     

*Always preserve your original dataset,and any variable transforms often should be new variables themselves.  
Analysis


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*Build your analysis plan from a list of hypotheses you're trying to validate/disprove; if you don't have a list of hypotheses a list of questions is almost as good.  Another technique is to storyboard a report / presentation you'd like to make with generic points, then start trying to build those pages/points by seeing what the data suggests.  

*Non-parametric can mean a few different things, but a good starting point are basic statistics like cross-tabs, t-tests, chi-square tests. Descriptively explore your data first, then explore the relations you find via crosstabs. You can do many tests of association, but read your software's documentation to verify whether the test is for nominal/categorical data or ordinal scale data. 

*Most software will let you apply things like linear regression to ordinal data once it's in that form - you just have to be careful. This could make sense for your likert scale data - or better yet use ordinal regression.

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*If doing any form of regression just make sure that any multi-value variable you're using is truly a scale value.  If it's categorical value without scale then transform the values into dummy (dichotomous) variables and use those instead.


*Always keep sample size top of mind.


Sounds like you're in SPSS - a quick google search turned up this link you might find useful for SPSS and likert scale data: http://www.uni.edu/its/support/article/604#chi
