Preparing Questionnaire Data for SPSS Analysis

Reliable results start with an organized dataset. Tests run on unchecked data can produce polished tables that still contain coding or missing-data errors.

Create a data dictionary

Document each variable name, question text, data type, response codes, and missing-value codes. Keep variable names short, clear, and consistent.

Code responses consistently

Use the same codes for repeated categories and avoid mixing text with numbers in one variable. Clearly document the direction of every Likert scale.

Handle reverse-scored items

Reverse negatively worded items before calculating a total score. On a 1-to-5 scale, the reversed score equals 6 minus the original score.

Check missing and unusual values

Run frequencies to detect values outside the permitted range, then choose a missing-data approach based on its amount and pattern. Document any replacement or exclusion decision.

Assess reliability before scoring

Use an appropriate reliability measure such as Cronbach's alpha when justified, and inspect item performance. Do not remove an item solely to raise reliability without a theoretical reason.

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