Questionnaire Design Guide

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

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

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]

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)

# 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)

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

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

# 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