Surveys and questionnaires - Science topic

Since you are working with open-ended questions, you could develop a coding system that specifies what kind of answers justify a Yes or No response to your filter question. That would allow you to exclude answers that do not match your specifications. Note that most of the above responses to do not take into account the availability of qualitative data to interpret your responses.

Thank you for your answer!

the problem is that the new questionnaire will need to be tested in terms of reliability and validity

In my opinion it depends on the context. A questionnaire is an instrument that collects information by means of a series of closed or open questions. The notion of an instrument is more general than that of a questionnaire, and encompasses individual and group interviews, systematic or participant observation, tests, scales, checklists, sorting, experiments, etc. Regarding your question, I think the more specific term should be used.

Sorry I don't have children. Will share it with those who do. Good luck with your research.

The usual reason to recommending odd numbers of response categories is to create a "middle category" for those who do not lean either direction on a topic. But that is not relevant here, so you could certainly use 6 response categories, if that is easier to set up.

Dear Mohammed Jaafar Ali Alatabe,

Thanks and invite you to joint discussions on the subject of innovation in financial institutions.

Regards,

Dariusz Prokopowicz

depends on measure.

You could have a look at Huang et al. (2019) for insights. Here is the full citation.

Huang, Y.-C., Backman, S. J., Backman, K. F., McGuire, F. A. & Moore, D. (2019). An investigation of motivation and experience in virtual learning environments: A self-determination theory. Education and Information Technologies, 24(1), 591–611. https://doi.org/10.1007/s10639-018-9784-5

Good luck,

Early in my career I encountered Anne Anastasi's book "Psychological Testing". I was struck, and have remained impressed all my life, by her thought that a "questionnaire is a sample of behaviour". That is, a questionnaire is a *sample* from which the analyst makes predictions about the *ensemble* of behaviours the respondent may be said to exhibit. It makes complete sense therefore to investigate the ability of a questionnaire to allow the prediction to be made with validity (does the sample really measure the ensemble of interest?) and reliability (does the sample fluctuate at random?) Using unvalidated questionnaires is, as I used to drone to my dear students till they knew this lesson, pseudo-science and snake-oil. As in all inferential statistics, the actual *data* (in the original question, "the scores") is an accident that has just taken place. What the investigator wants to know is: what does the accident tell us ("the questionnaire")? Welcome to the wonderful world of Psychometrics!

It is correct you should only present evidence of validity according to your objectives that are based on the inferences of the scores obtained from your study. You should consider for the evidence of internal validity the confirmatory factor analysis.

Valuable inputs from RG colleagues

I'm glad my comments were useful. You will also find that if a question is shaped primarily by analytical concerns, you will likely not be able to translate the question into everyday language that will be easily understood. If we ask a survey question in any language, and we get a puzzled look, then we have failed to do our job. A good survey question is one for which you know in advance more or less what answers people will give; you just don't know how many will give which of the 3-5 likely answers.

Congratulations on your high response rate. I guess the follow up will be via email as well and online FU's or online recruitment is not somethin I am familiar with but I guess the main thing is to send out the FU reuqest early enough for the participant to get back to you as well. Also - we get so many emails from different companies that unless it is somethin we are expecting or someting that were familiar with we may just skip past the email without realising. So - you could send out an initial email - reminding the participants of the upcoming questionnaire and then send out another one with the link attached. In both communications, make sure you make it clear in the email subject / header that this email is regarding study / project that they agreed to participate in to jog their memory and to make it stand out from all their other emails. Hope this helps

Hello Eleftherios,

There are numerous texts on how to do this, but here's a very basic and simple starting point:

Good luck with your work!

There are many guidelines available for translation and adaptation of a questionnaire from its original language (usually English) to a target language (your country national language). Examples include:

(1)Beaton, D. E., Bombardier, C., Guillemin, F. & Ferraz, M. B. (2000). Guidelines for the Process of Cross-Cultural Adaptation of Self-Report Measures. Spine, 25(24), 3186-3191.

(2) Guillemin, F., Bombardier, C. & Beaton, D. (1993). Cross-cultural adaptation of health-related quality of life measures: literature review and proposed guidelines. J Clin Epidemiol, 46(12), 1417-1432.

Both are the most cited papers for translation and adaptation of scales to another language. Generally, for adaptation, you need to carry out forward translations (at least two), synthesis of forward translations into one common translations by an expert committee (consist of translators and expert of the fields), back translation of the common translation to original language (English), expert committee review to finalize the translation, pre-testing of the translated version of scale, another expert committee meeting to finalize the scale, and lastly validation of the scale of its reliability and validity.

attached file may help....

Positively,it is highly focussed and more cost effective. Negatively, it is highly selective that it can miss out vital data from other related diseases.

Hello Alexander,

Yes, it is preferable to present evidence for score reliability and validity (for the intended use of scores) for any new measure. Doing so helps justify the use of your measure(s) in your own study, as well as to guide other researchers who might wish to use or adapt your measure(s).

In this case of your study, who's to know whether the rational groupings you discuss at the end of your question will correspond to empirically derived groupings of the items?

Good luck with your work.

The answer was exactly what I was looking for before sleeping, and surprised when I woke-up! Once again many thanks for the interesting explanations, comments and the worthy compliments Dr. Paul. More-or-less similar reasoning behind me when I decided to study in the field of Civil-Engineering almost 15 years back (I returned to academic ambient again after an approximately 7-8 years of absence, mostly struggling with new experiences at construction-sites in Iran). It would be reciprocally my great honour too, although there is a long way to become such a professional researcher like you.

I don't think there is such a website. You can find such instruments in studies written in German.

Dear Najla Alsiri , thank you very much for your feedback.

Let's go step by step

1) Group membership predicts a change in score in Questionnaire A.

ANOVA or ANCOVA adjusted for baseline value of Questionnaire A and probably also for gender and age

First of all you can apply the same ANOVA / ANCOVA model as above for each of 3 componensts of Questionnaire A and that with the lowest p-value (highest test statistics) will be winner = component most affected by group membership.

Another point of view is to take one of 4 groups and which of the three topics of Questionnaire A is most affected by this subgroup. By decriptive statistics select one with the highest absolute mean change. Then you can calculate difference between this component and each of other 3 components of Questionnaire A. For these 3 difference you can apply paired t-test (or paired Wilcoson signed rank test if distributions are far from gaussian)

ANOVA or ANCOVA adjusted for baseline value of Questionnaire B and probably also for gender and age

Linear Regression: Changes A is predictor and changes B is the outcome is one option. You should start with scatter-plot Questionnaire A on x-axis, Questionnaire B on y-axis. But class variable group (there are 4 categorial groups here) should be also included and probably also age and gender. First of all you should check if the linear relation is an appropriate model. If not then some non-linear model could be more appropriate (scatter-plot pathern could help to select which non-linear model)

Linear Regression model - stepwise selection with all 3 components of Questionnaire A, group effect (4 groups), age, gender (and may be another baseline characteristics as predictors if available) will be the best option.