If you are asking how to design a survey questionnaire, you are really asking how to convert theory into measurable evidence. A survey questionnaire is a structured instrument used to operationalise abstract constructs so they can be observed and analysed. In postgraduate research, it forms the foundation of your empirical argument. Weak measurement weakens everything. Strong measurement strengthens everything.
This guide outlines the essential steps in survey questionnaire design, with attention to alignment, Likert scale structure, bias control, and expert validation.
Start with Clear Conceptual Alignment
Good questionnaire design begins before any item is written. Each research objective must correspond to clearly defined constructs, and each construct must be grounded in theory.
Suppose your objective is to examine the influence of organizational strategy on resource allocation. That objective must be unpacked. Organizational strategy may include cost leadership, differentiation, or focus orientation. Resource allocation may involve labour, capital investment, marketing expenditure, or staff training. Each construct should be defined precisely before drafting statements.
Once dimensions are clear, generate items that map directly to them. Every statement must trace back to a construct and then to an objective. If it cannot, remove it. This alignment protects content validity and reduces instability during confirmatory factor analysis or SEM.
Likert Scale Questionnaire Design and Structure
Most postgraduate surveys use Likert type scales to measure perceptions, behaviours, or attitudes. Yet Likert scale questionnaire design requires more care than many assume.
Five point scales are widely used because they reduce respondent fatigue (Leavy, 2022). Seven point scales provide greater variance and may enhance sensitivity in structural modelling when sample size is adequate. The decision should reflect your analytical plan and respondent profile.
What matters more than scale length is structure and consistency.
A properly structured agreement scale should
• move logically from negative to positive
• maintain balanced response categories
• avoid unnecessary mixing of scale formats
For example
1 Strongly disagree
2 Disagree
3 Neither agree nor disagree
4 Agree
5 Strongly agree
Switching between agreement and frequency scales within the same construct increases cognitive load and can introduce measurement error.
Reverse coded items are sometimes used to reduce acquiescence bias. However, excessive reversed statements may distort factor structures and introduce method effects, particularly in CFA. If used, they should be clear and limited.
Clarity also matters at the item level. Each statement should measure one idea only. Double barreled questions weaken reliability and complicate interpretation.
Avoiding Measurement Bias in Survey Research
Even well aligned instruments can suffer from bias. Leading wording pushes respondents toward agreement. Social desirability bias encourages favourable responses, especially in organisational settings. Common method bias may inflate relationships when all variables are measured from the same source at the same time.
Bias is best controlled during design. Use neutral wording. Assure anonymity. Separate constructs logically within the questionnaire. Prevention at this stage is far more effective than statistical correction later.
Expert Validation and Pilot Testing
Before distributing your questionnaire widely, subject it to expert review. Experts should assess whether items adequately represent the constructs, whether wording is precise, and whether important dimensions are missing.
A structured relevance rating approach can be used to quantify agreement and refine weak items. After revision, conduct a pilot study to test reliability and preliminary factor structure. Low loadings or ambiguous feedback signal the need for further refinement.
Skipping validation often results in poor model fit or unstable measurement later.
Frequently Asked Questions on Survey Questionnaire Design
Typically three to five well designed items are sufficient for reflective constructs. Too few threaten reliability. Too many create redundancy.
Where possible, adapt validated scales from reputable studies. Ensure conceptual equivalence and contextual relevance.
Both are acceptable. Seven point scales often provide greater statistical sensitivity for advanced modelling, provided respondents can handle the distinction.
After drafting the instrument but before pilot testing.