Questionnaire Design Guide

Questionnaire Design Guide

Design valid and reliable survey instruments with proper question types, Likert scale construction, response coding, and data preparation for analysis.

Survey Design Principles

Question Types

The Four C's of Good Questions

1. Clear: Avoid jargon, double-barreled questions, and ambiguity
2. Concise: Keep questions short (ideally under 20 words)
3. Complete: Include all relevant response options
4. Consistent: Use the same scale direction and format throughout

Likert Scale Design

Scale Points

Anchoring Labels

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5-Point Agreement Scale:
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree

5-Point Frequency Scale:
1 = Never
2 = Rarely
3 = Sometimes
4 = Often
5 = Always

5-Point Satisfaction Scale:
1 = Very Dissatisfied
2 = Dissatisfied
3 = Neutral
4 = Satisfied
5 = Very Satisfied

Reverse-Coded Items

Include 2-3 reverse-coded items per construct to detect acquiescence bias:

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Regular:  "I find research methods interesting."        (1-5: SD to SA)
Reversed: "I find research methods tedious and dull."   (1-5: SD to SA)

# Recode reversed items before analysis:
# reversed_score = (max_scale + 1) - raw_score
# For a 5-point scale: reversed_score = 6 - raw_score

Constructing a Multi-Item Scale

Step-by-Step Process

1. Define the construct: Write a clear conceptual definition
2. Generate items: Write 1.5-2x the number of items you plan to keep (e.g., write 15 items for an 8-item scale)
3. Expert review: Have 3-5 experts rate each item for relevance (Content Validity Index)
4. Pilot test: Administer to 30-50 respondents
5. Item analysis: Calculate item-total correlations, check reliability
6. Exploratory Factor Analysis (EFA): Confirm dimensionality
7. Finalize scale: Remove weak items, re-test reliability

Example: Research Self-Efficacy Scale

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Construct: Belief in one's ability to conduct academic research

Items (5-point Likert, Strongly Disagree to Strongly Agree):
RSE1: I can formulate clear research questions.
RSE2: I can design an appropriate research methodology.
RSE3: I can analyze data using statistical software.
RSE4: I can write a publishable research paper.
RSE5: I can critically evaluate published research.
RSE6: I can present research findings at a conference.
RSE7R: I struggle to interpret statistical results. [REVERSED]
RSE8R: I find it difficult to synthesize literature. [REVERSED]

Data Coding and Preparation

Coding Scheme

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import pandas as pd
import numpy as np

# Define coding scheme
likert_coding = {
    "Strongly Disagree": 1,
    "Disagree": 2,
    "Neither Agree nor Disagree": 3,
    "Agree": 4,
    "Strongly Agree": 5
}

# Apply coding
df["Q1_coded"] = df["Q1_raw"].map(likert_coding)

# Reverse code specific items
reverse_items = ["RSE7R", "RSE8R"]
max_scale = 5
for item in reverse_items:
    df[f"{item}_recoded"] = (max_scale + 1) - df[item]

# Calculate composite score (mean of items)
scale_items = ["RSE1", "RSE2", "RSE3", "RSE4", "RSE5", "RSE6",
               "RSE7R_recoded", "RSE8R_recoded"]
df["RSE_mean"] = df[scale_items].mean(axis=1)

Missing Data Handling

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# Check missing data patterns
print(df[scale_items].isnull().sum())
print(f"Complete cases: {df[scale_items].dropna().shape[0]} / {df.shape[0]}")

# Common strategies:
# 1. Listwise deletion (if < 5% missing)
df_complete = df.dropna(subset=scale_items)

# 2. Mean imputation per item (simple but biased)
df[scale_items] = df[scale_items].fillna(df[scale_items].mean())

# 3. Person-mean imputation (if < 20% of items missing per person)
def person_mean_impute(row, items, max_missing=2):
    if row[items].isnull().sum() <= max_missing:
        return row[items].fillna(row[items].mean())
    return row[items]  # leave as NaN if too many missing

df[scale_items] = df.apply(lambda r: person_mean_impute(r, scale_items), axis=1)

Reliability Analysis

Cronbach's Alpha

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import pingouin as pg

# Calculate Cronbach's alpha
alpha = pg.cronbach_alpha(df[scale_items])
print(f"Cronbach's alpha: {alpha[0]:.3f}")
# Interpretation: >= 0.70 acceptable, >= 0.80 good, >= 0.90 excellent

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library(psych)

# Cronbach's alpha with item-level diagnostics
alpha_result <- alpha(data[, scale_items])
print(alpha_result)
# Check "raw_alpha if item dropped" to identify weak items

Item-Total Correlations

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# Corrected item-total correlations (should be > 0.30)
item_stats <- alpha_result$item.stats
print(item_stats[, c("r.drop", "raw.alpha")])
# r.drop < 0.30: consider removing the item
# raw.alpha increases if dropped: item is weakening the scale

Validity Assessment

Common Design Mistakes

Survey Platform Comparison