SPSS can do this pretty well, but, it is expensive, slow, and has a really lame internal syntax language. But, you may also want to produce a cross tab that shows you count/percent for each category column. in a Disagree Agree scale is to get a mean response / st dev. The common use case for this is to see what the average scale rating is for a question - e.g.

In pandas, I think it would be easy to treat all of these as category labels with pd.categorical. the trouble is that for many types of analysis you want both. 'Strongly Agree' will be what is in each cell where that was selected) or, you can have a '5'. If you export a survey file as CSV from any of those tools, you are presented with the choice to either take the strings from each questions (e.g.

Sometimes, that missing data is also coded as a -99 or other values.įinally, SPSS has 2 properties, VARIABLE LABELS and VALUE LABELS that contain the Strings that tend to correspond to question text/choice text.

for each of the possible choices, and then each cell has a 1 for Selected and a null/SYSMIS/NaN value if it was not selected. If you can multi-select, most survey tools export questions like Q2_1, Q2_2, Q2_3. For single select questions or Open ended, the values are usually coded as a single number per choice, so a column may have a 1, 2, or 3 for male, female, or 'prefer not to state'. Survey data seems to always be represented as one row per response and one column per questions or question choice. Addressing any of these questions will greatly benefit the end-users of research, the people whose daily lives and decisions will be directly affected by generating high quality research evidence.SPSS is the only file format that is exportable from common survey tools like Qualtrics, SurveyGizmo, and SurveyMonkey that allows you to preserve both the values and the labels for many variables. The other nine questions were about making treatment safe and effective, understanding why people with PA vary in their need for treatment, links to other conditions, and how to encourage clinicians to take PA seriously and provide long-term care.Ĭonclusions This JLA PSP enabled patients, carers and clinicians to work together to agree the Top 10 uncertainties relating to the cause, diagnosis, management and treatment of PA. The number one question was about an accurate and reliable diagnostic test for PA. These were discussed at the final workshop to agree the final Top 10. The combined rankings from the 1068 patients, carers and clinicians who took part in the second survey identified a short list of 16 questions. None had been answered by previous research. Results In the first survey, 933 people submitted 3480 responses that were categorised and summarised to generate a long list of 40 questions. An online workshop used an adapted nominal group technique to agree a final Top 10. A second survey asked respondents to identify and rank their top 10 questions from the list of questions from the first survey. A literature search checked whether any of these questions had already been answered. A first survey identified questions relating to the cause, diagnosis, treatment and management of PA. Objectives To form a James Lind Alliance (JLA) Priority Setting Partnership (PSP) to determine research priorities related to the cause, diagnosis, treatment and management of pernicious anaemia (PA) from the perspectives of patients, carers and clinicians.ĭesign The PSP conducted two surveys and a workshop to identify the Top 10 questions for research.

Pspp exports survey monkey