Survey Design Course

Lesson 1 • Ethical Principles in Survey Research

Covers informed consent, confidentiality, data minimisation, and voluntary participation. Establishes non-negotiable ethical standards that govern every design decision in the course.

Lesson 2 • Types of Survey Designs

Compares cross-sectional, longitudinal, and panel designs by purpose, cost, and data quality. Equips learners to select the right design before any questionnaire work begins.

Lesson 3 • Identifying a Research Problem

Translates a vague information need into a precise, answerable research problem. Provides the diagnostic lens used throughout the course to evaluate design decisions.

Lesson 1 • Validity in Survey Measurement

Explains content, construct, and criterion validity and how each is established or threatened. Directly informs item selection and questionnaire review processes covered later.

Lesson 2 • Rating and Response Scales

Compares Likert, semantic differential, numeric rating, and visual analog scales by their measurement properties. Enables informed scale selection for any attitude or behaviour construct.

Lesson 3 • Reliability of Survey Instruments

Covers test-retest, internal consistency, and inter-rater reliability as indicators of measurement stability. Reliability assessment is a prerequisite for the pilot-testing chapter.

Lesson 4 • Core Concepts of Measurement

Introduces constructs, variables, and the measurement process that links abstract concepts to observable indicators. Provides the theoretical backbone for all question-writing decisions.

Lesson 1 • Visual and Layout Design

Applies formatting, white space, and visual hierarchy principles to paper and digital questionnaires. Layout directly affects response accuracy and completion rates.

Lesson 2 • Response Option Design

Covers exhaustiveness, mutual exclusivity, and balance in response categories for closed questions. Well-designed options prevent forced errors and improve data quality.

Lesson 3 • Writing Clear Survey Questions

Identifies and eliminates double-barrelled, leading, loaded, and ambiguous wording. Clear questions reduce measurement error and are the foundation of valid data.

Lesson 4 • Questionnaire Flow and Structure

Addresses question ordering, skip logic, funneling, and section grouping to reduce respondent burden. Proper flow increases completion rates and data integrity.

Lesson 1 • Sample Size Determination

Teaches margin-of-error, confidence level, and effect-size approaches to calculating required sample sizes. Accurate sizing prevents both underpowered and wastefully large studies.

Lesson 2 • Population and Sampling Concepts

Defines target population, sampling frame, and the unit of analysis as prerequisites for any sampling decision. Misdefining the population is the most common source of sampling error.

Lesson 3 • Probability Sampling Methods

Covers simple random, systematic, stratified, and cluster sampling with their trade-offs in precision and cost. Probability methods are required when statistical inference to a population is the goal.

Lesson 4 • Sampling Error and Bias

Distinguishes sampling error from nonresponse, coverage, and measurement bias and explains mitigation strategies. Understanding bias sources is essential before data collection begins.

Lesson 1 • Overview of Survey Modes

Introduces the four primary modes and the key dimensions—cost, speed, coverage, and response quality—used to compare them. Mode choice shapes every downstream design and analysis decision.

Lesson 2 • Response Rate Optimisation

Applies tailored design, reminder scheduling, and incentive strategies to maximise response rates across modes. Response rate directly affects nonresponse bias and sample representativeness.

Lesson 3 • Mixed-Mode Survey Strategies

Explains sequential and concurrent mixed-mode designs and how to minimise mode effects on data comparability. Mixed-mode designs improve coverage but introduce measurement challenges.

Lesson 4 • Interviewer-Administered Surveys

Addresses interviewer training, standardisation, and quality control for telephone and face-to-face modes. Interviewer effects are a unique source of error requiring active management.

Lesson 1 • Cognitive Interviewing Techniques

Uses think-aloud and verbal probing protocols to uncover how respondents interpret and answer questions. Cognitive testing reveals comprehension failures invisible to the designer.

Lesson 2 • Finalising the Survey Instrument

Integrates all pretest findings into a final revision cycle and documents changes for the methodology record. A documented revision history supports transparency and replication.

Lesson 3 • Expert Review and Peer Evaluation

Structures systematic expert panels to assess content validity, wording, and scale coverage before field testing. Expert review catches construct-level problems that respondent testing may miss.

Lesson 4 • Quantitative Pretesting

Conducts a small-scale field pretest to assess item variance, skip-logic accuracy, and completion time. Quantitative pretesting bridges cognitive testing and full deployment.

Lesson 1 • Data Cleaning and Validation

Applies range checks, logic checks, and duplicate detection to produce a clean, analysis-ready dataset. Data cleaning decisions must be documented to ensure reproducibility.

Lesson 2 • Monitoring Data Collection Progress

Tracks response rates, completion patterns, and early data distributions to detect problems during fieldwork. Real-time monitoring enables corrective action before data quality is compromised.

Lesson 3 • Handling Nonresponse During Fieldwork

Applies targeted follow-up, mode switching, and subgroup analysis to address nonresponse as it occurs. Proactive nonresponse management reduces bias more effectively than post-hoc weighting alone.

Lesson 4 • Data Entry and Coding Procedures

Covers manual entry verification, optical scanning, and coding schemes for open-ended responses. Consistent coding is the bridge between raw responses and analysable data.

Lesson 1 • Descriptive Analysis of Survey Data

Applies frequency distributions, central tendency, and dispersion measures to summarise survey responses. Descriptive analysis is the mandatory first step before any inferential work.

Lesson 2 • Analysing Open-Ended Responses

Applies thematic coding, content analysis, and frequency-of-theme reporting to qualitative survey data. Open-ended analysis enriches quantitative findings with respondent language and context.

Lesson 3 • Inferential Analysis Techniques

Applies chi-square, t-tests, ANOVA, and correlation to test hypotheses derived from research objectives. Inferential tests must be matched to the measurement level of the variables involved.

Lesson 4 • Handling Missing Data

Distinguishes MCAR, MAR, and MNAR missing data mechanisms and applies listwise, pairwise, and imputation solutions. Missing data treatment affects every subsequent analysis and must be justified.