Survey Data Processing

Survey Data Processing

A skill for cleaning, recoding, and preparing survey response data for statistical analysis. Covers handling common survey data issues such as incomplete responses, attention check failures, reverse-coded items, scale construction, open-ended response coding, and export to analysis-ready formats compatible with SPSS, Stata, and R.

Survey Data Quality Assessment

Initial Inspection Workflow

Survey data from platforms like Qualtrics, SurveyMonkey, REDCap, and Google Forms each have their own export formats and quirks. The first step is always standardization.

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import pandas as pd
import numpy as np

def assess_survey_quality(df, duration_col="duration_seconds",
                          min_duration=60):
    """
    Generate a survey data quality report.

    Checks:
    - Completion rates per question
    - Response duration (speeders and slow responders)
    - Straight-line responding patterns
    - Attention check failures
    """
    report = {}

    # Overall completion
    total_respondents = len(df)
    complete = df.dropna(thresh=int(len(df.columns) * 0.8))
    report["total_responses"] = total_respondents
    report["substantially_complete"] = len(complete)
    report["completion_rate"] = f"{len(complete)/total_respondents*100:.1f}%"

    # Duration analysis
    if duration_col in df.columns:
        durations = df[duration_col].dropna()
        report["median_duration_seconds"] = durations.median()
        report["speeders"] = (durations < min_duration).sum()
        report["speeder_pct"] = f"{(durations < min_duration).mean()*100:.1f}%"

    # Missing data per question
    missing_by_col = df.isna().sum().sort_values(ascending=False)
    report["most_skipped_questions"] = missing_by_col.head(10).to_dict()

    return report

Identifying Low-Quality Responses

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def detect_straightlining(df, likert_columns, threshold=0.9):
    """
    Detect respondents who select the same answer for nearly
    all Likert-scale questions (straight-line responding).

    A respondent is flagged if the proportion of their most
    common response exceeds the threshold.
    """
    flagged = []
    for idx, row in df[likert_columns].iterrows():
        responses = row.dropna()
        if len(responses) == 0:
            continue
        most_common_pct = responses.value_counts().iloc[0] / len(responses)
        if most_common_pct >= threshold:
            flagged.append(idx)

    return flagged


def check_attention_items(df, attention_checks):
    """
    Validate attention check (trap) questions.

    Args:
        attention_checks: dict of {column_name: correct_answer}
        Example: {"q15_attention": 4, "q32_trap": "strongly agree"}
    """
    failed = pd.Series(False, index=df.index)
    for col, correct in attention_checks.items():
        failed = failed | (df[col] != correct)

    return df.index[failed].tolist()

Recoding and Transformation

Reverse Coding

Many validated psychological scales include reverse-coded items to detect acquiescence bias. These must be recoded before computing scale scores.

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def reverse_code(df, columns, scale_max, scale_min=1):
    """
    Reverse-code specified columns for Likert-type scales.

    Formula: reversed = (scale_max + scale_min) - original

    Example for a 1-5 scale:
      1 -> 5, 2 -> 4, 3 -> 3, 4 -> 2, 5 -> 1
    """
    df_recoded = df.copy()
    for col in columns:
        df_recoded[col] = (scale_max + scale_min) - df[col]
    return df_recoded


# Example usage with a Big Five personality scale
reverse_items = {
    "extraversion": ["ext_2", "ext_4", "ext_6"],
    "neuroticism": ["neur_1", "neur_3", "neur_5"],
    "agreeableness": ["agree_3", "agree_5"],
}

# For a 1-7 Likert scale:
for construct, items in reverse_items.items():
    df = reverse_code(df, items, scale_max=7, scale_min=1)

Scale Construction

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def compute_scale_scores(df, scale_definitions, method="mean"):
    """
    Compute composite scale scores from individual items.

    Args:
        scale_definitions: dict mapping scale name to list of columns
        method: "mean" or "sum"

    Returns:
        DataFrame with new scale score columns
    """
    for scale_name, items in scale_definitions.items():
        if method == "mean":
            df[scale_name] = df[items].mean(axis=1)
        elif method == "sum":
            df[scale_name] = df[items].sum(axis=1)

        # Also compute Cronbach's alpha for reliability
        alpha = cronbachs_alpha(df[items])
        print(f"{scale_name}: alpha = {alpha:.3f} "
              f"(n_items = {len(items)})")

    return df


def cronbachs_alpha(item_df):
    """
    Compute Cronbach's alpha for internal consistency reliability.
    Values above 0.70 are generally considered acceptable.
    """
    item_df = item_df.dropna()
    n_items = item_df.shape[1]
    if n_items < 2:
        return np.nan

    item_variances = item_df.var(axis=0, ddof=1)
    total_variance = item_df.sum(axis=1).var(ddof=1)

    alpha = (n_items / (n_items - 1)) * (
        1 - item_variances.sum() / total_variance
    )
    return alpha

Open-Ended Response Processing

Coding Qualitative Responses

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def code_open_responses(df, text_column, codebook):
    """
    Apply a predefined codebook to open-ended responses using
    keyword matching. For research-quality coding, this should
    be supplemented with manual coding by trained raters.

    Args:
        codebook: dict mapping code names to keyword lists
        Example: {
            "financial_concern": ["money", "cost", "expensive", "afford"],
            "time_constraint": ["time", "busy", "schedule", "hours"],
            "quality_issue": ["quality", "broken", "defect", "poor"],
        }
    """
    for code_name, keywords in codebook.items():
        pattern = "|".join(keywords)
        df[f"code_{code_name}"] = (
            df[text_column]
            .str.lower()
            .str.contains(pattern, na=False)
            .astype(int)
        )

    return df

Inter-Rater Reliability

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When multiple coders classify open-ended responses:

Cohen's Kappa (2 raters):
  - < 0.20: poor agreement
  - 0.21-0.40: fair
  - 0.41-0.60: moderate
  - 0.61-0.80: substantial
  - 0.81-1.00: almost perfect

Fleiss' Kappa (3+ raters):
  - Same interpretation scale as Cohen's
  - Use when more than two raters code the same responses

Process:
  1. Develop codebook with definitions and examples
  2. Train coders on 10-20 practice responses
  3. Code 20% of responses independently (overlap set)
  4. Calculate inter-rater reliability on the overlap set
  5. If kappa < 0.70, discuss disagreements and refine codebook
  6. Repeat until acceptable reliability is achieved
  7. Divide remaining responses among coders

Data Reshaping for Analysis

Wide to Long Format

Survey data is typically exported in wide format (one row per respondent, one column per question). Many analyses require long format.

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def reshape_repeated_measures(df, id_col, time_points,
                              measure_prefix):
    """
    Reshape repeated-measures survey data from wide to long.

    Example: columns q1_pre, q1_post -> long format with
    time column ("pre", "post") and value column.
    """
    value_vars = [f"{measure_prefix}_{t}" for t in time_points]

    long_df = pd.melt(
        df,
        id_vars=[id_col],
        value_vars=value_vars,
        var_name="time_point",
        value_name=measure_prefix
    )

    # Clean time_point column
    long_df["time_point"] = (
        long_df["time_point"]
        .str.replace(f"{measure_prefix}_", "")
    )

    return long_df

Export for Statistical Software

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Export formats by software:

SPSS (.sav):
  - Use pyreadstat: pyreadstat.write_sav(df, "output.sav")
  - Include variable labels and value labels
  - Set measurement level (nominal, ordinal, scale)

Stata (.dta):
  - Use pandas: df.to_stata("output.dta")
  - Include variable labels via write_stata with labels dict

R (.csv with codebook):
  - Export CSV plus a separate codebook document
  - Or use pyreadstat to write .rds format
  - Include factor level definitions

General best practices:
  - Include a unique respondent ID column
  - Use numeric codes for categorical variables (with labels)
  - Document all recoding in a companion codebook
  - Save both raw and processed versions
  - Include a timestamp column for data versioning

Proper survey data processing is essential for valid statistical inference. Decisions made during cleaning and recoding directly affect research conclusions, making transparent documentation of every step a methodological requirement rather than a convenience.