7.1 Survey research fundamentals
3 min read•july 23, 2024
Survey research is a powerful tool for gathering data from large groups. It uses standardized questions to collect information on attitudes, behaviors, and intentions, allowing for quantitative analysis and generalization to broader populations.
While surveys are cost-effective and allow for large sample sizes, they have limitations. These include potential biases and challenges in establishing causality. Proper sampling techniques and administration modes are crucial for obtaining reliable and representative data.
Survey Research Fundamentals
Key characteristics of survey research
Top images from around the web for Key characteristics of survey research
Frontiers | Questionnaire Measures and Physiological Correlates of Presence: A Systematic Review View original
Is this image relevant?
Quantitative Data Analysis Software - Research leap View original
Is this image relevant?
Frontiers | Quantitative Data From Rating Scales: An Epistemological and Methodological Enquiry View original
Is this image relevant?
Frontiers | Questionnaire Measures and Physiological Correlates of Presence: A Systematic Review View original
Is this image relevant?
Quantitative Data Analysis Software - Research leap View original
Is this image relevant?
1 of 3
Top images from around the web for Key characteristics of survey research
Frontiers | Questionnaire Measures and Physiological Correlates of Presence: A Systematic Review View original
Is this image relevant?
Quantitative Data Analysis Software - Research leap View original
Is this image relevant?
Frontiers | Quantitative Data From Rating Scales: An Epistemological and Methodological Enquiry View original
Is this image relevant?
Frontiers | Questionnaire Measures and Physiological Correlates of Presence: A Systematic Review View original
Is this image relevant?
Quantitative Data Analysis Software - Research leap View original
Is this image relevant?
1 of 3
- Systematic data collection method gathers information from a sample of a population using a structured approach
- Uses standardized questionnaires or interviews to ensure consistency and comparability across respondents
- Allows for quantitative analysis of responses by converting data into numerical form for statistical analysis
- Suitable for gathering data on attitudes (opinions on a topic), behaviors (past actions), and intentions (future plans)
- Can be conducted via various modes such as online (web-based), telephone, face-to-face, or mail
Advantages vs limitations of surveys
- Cost-effective compared to other methods like focus groups or experiments, especially for large sample sizes
- Allows for large sample sizes and generalizability to the broader population when using probability sampling
- Standardized questions ensure consistency and comparability across respondents and time periods
- Limited ability to establish causality or explore complex issues in-depth compared to qualitative methods
- Potential for (answering inaccurately) and social desirability bias (answering to appear favorable)
- Reliance on self-reported data, which may be inaccurate or incomplete due to memory lapses or lack of knowledge
- Challenges in ensuring high response rates (percentage who complete the survey) and sample representativeness
Sampling in survey research
- Allows for inferring characteristics of a population from a smaller subset to save time and resources
- Probability ensure each member of the population has a known chance of being selected
- Simple random sampling selects participants purely by chance from a list of the population (phone book)
- Stratified sampling first divides the population into subgroups (gender), then randomly samples from each
- Cluster sampling randomly selects groups (city blocks), then samples all individuals within those groups
- Non-probability sampling methods do not rely on random selection and may introduce bias
- Convenience sampling selects participants who are easy to reach (mall shoppers, online panels)
- Snowball sampling asks initial participants to recruit additional participants from their networks
- Quota sampling selects participants based on predetermined characteristics (age brackets) until quotas are met
Modes of survey administration
- conducted via the internet using web-based platforms (SurveyMonkey) or email
- Cost-effective, quick to deploy and analyze, wide geographic reach, convenient for respondents
- Potential for coverage bias (some groups less likely to have internet access), self-selection bias
- Telephone surveys conducted by interviewers over the phone using a structured questionnaire
- Allows personal interaction to clarify questions, can achieve higher response rates than online
- Increasing difficulty in reaching respondents (caller ID), potential for interviewer bias in delivery
- Face-to-face surveys conducted in person by interviewers using a structured questionnaire
- Highest response rates, ability to use visual aids (product images) and observe non-verbal cues
- Time-consuming, expensive, potential for interviewer bias and social desirability bias
- Mail surveys send questionnaires to respondents' physical addresses with return envelopes
- Can reach populations without internet/phone access, respondents can complete on their own time
- Slow turnaround time, low response rates, no control over who actually fills out the questionnaire
Key Terms to Review (18)
Closed-ended questions: Closed-ended questions are types of survey questions that provide respondents with specific options to choose from, typically limiting their answers to predefined responses. This format makes it easier to analyze data quantitatively, as each response can be categorized and compared across participants. Closed-ended questions are crucial for ensuring clarity and consistency in responses, influencing how measurement scales, survey designs, question phrasing, and overall questionnaire flow are structured.
Confidence Interval: A confidence interval is a range of values, derived from a data set, that is likely to contain the true population parameter with a specified level of confidence, typically expressed as a percentage. This statistical concept provides a way to quantify uncertainty around an estimate, helping researchers understand how reliable their findings are. The width of the interval reflects the precision of the estimate, and it is influenced by factors such as sample size and variability.
Cross-sectional survey: A cross-sectional survey is a research method used to collect data from a specific population at a single point in time. This approach provides a snapshot of the population's characteristics, opinions, or behaviors, allowing researchers to analyze patterns and correlations without the influence of time. Cross-sectional surveys are commonly utilized in social sciences, market research, and health studies to gather quantitative information efficiently.
Descriptive Statistics: Descriptive statistics refers to a set of techniques used to summarize and organize data in a meaningful way, allowing researchers to present and interpret data effectively. These techniques include measures of central tendency and dispersion, providing insights into the overall trends and patterns within a dataset. Descriptive statistics is essential in various contexts such as research design, evaluating data quality, conducting surveys, cleaning and validating data, and analyzing data distributions.
External validity: External validity refers to the extent to which research findings can be generalized to and have relevance in real-world settings beyond the specific conditions of the study. It highlights the importance of being able to apply the results of a study to different populations, environments, and times, ensuring that conclusions drawn from research can be meaningful outside the controlled settings in which they were obtained.
Fieldwork: Fieldwork refers to the process of collecting data outside of a laboratory or controlled setting, typically through direct interaction with subjects or environments relevant to the research. This hands-on approach is essential for survey research, as it allows researchers to gather real-world insights and perspectives, making the findings more applicable and grounded in actual behavior and opinions.
Inferential Statistics: Inferential statistics involves techniques that allow conclusions to be drawn about a population based on a sample of data. It helps researchers make predictions or inferences about broader trends and relationships, relying on probability theory and various statistical models to analyze data collected from smaller groups.
Internal Validity: Internal validity refers to the extent to which a study accurately establishes a causal relationship between variables, ensuring that any observed effects are indeed due to the manipulation of the independent variable rather than other extraneous factors. It's crucial for establishing reliable conclusions about cause and effect in research. High internal validity means that the findings are trustworthy and can be confidently attributed to the intervention or treatment applied in the study design.
Longitudinal survey: A longitudinal survey is a research method that collects data from the same subjects repeatedly over a period of time, allowing researchers to observe changes and trends within the population. This approach helps in understanding how variables change and interact over time, making it valuable for tracking developments, behaviors, or attitudes in a given group. Longitudinal surveys can provide insights into causal relationships, helping to clarify the dynamics at play in various fields such as psychology, sociology, and market research.
Margin of Error: The margin of error is a statistical term that represents the amount of random sampling error in a survey's results. It indicates the range within which the true population parameter is expected to fall, providing a measure of the reliability of the data collected. A smaller margin of error suggests more confidence in the accuracy of the results, while a larger margin may indicate potential bias or inaccuracy, which is essential to understand during the research process, particularly when designing surveys, calculating sample sizes, and addressing errors.
Online surveys: Online surveys are data collection tools that utilize the internet to gather responses from participants about specific topics or research questions. They are designed to be easy for respondents to complete, often involving multiple-choice, rating scales, or open-ended questions. The convenience and accessibility of online surveys make them a popular choice for researchers looking to collect data quickly and efficiently.
Open-ended questions: Open-ended questions are inquiries that allow respondents to provide unrestricted answers, giving them the freedom to express their thoughts and opinions in their own words. This type of questioning is valuable as it encourages detailed responses and insights, helping to gather richer data for analysis in various research contexts.
Pilot Testing: Pilot testing refers to the preliminary trial run of a research study, often involving a smaller sample from the target population, to identify any potential issues in the research design, data collection methods, or questionnaire. This step is crucial for refining the research process, ensuring that the tools and techniques are effective and appropriate before full-scale implementation. By conducting pilot tests, researchers can gather insights on how well the survey questions perform, the clarity of instructions, and how the overall flow resonates with participants.
Questionnaire design: Questionnaire design is the process of creating a structured set of questions aimed at gathering information from respondents for research purposes. Effective questionnaire design is crucial because it determines the quality and reliability of the data collected, which directly impacts the insights gained from survey research. A well-designed questionnaire enhances response rates, reduces biases, and ensures clarity in understanding respondents' thoughts and opinions.
Response bias: Response bias refers to the tendency of survey respondents to answer questions inaccurately or falsely, often due to various influences such as social desirability, question phrasing, or misunderstanding. This phenomenon can significantly distort research findings and affect the validity of the data collected, making it crucial to identify and mitigate it during survey design and implementation.
Sampling bias: Sampling bias occurs when certain members of a population are systematically more likely to be selected for a sample than others, leading to results that do not accurately represent the entire population. This can significantly distort research findings and compromise the validity of conclusions drawn from the data. Addressing sampling bias is crucial in evaluating data quality and ensuring that survey research yields reliable insights.
Sampling methods: Sampling methods refer to the various techniques used to select a subset of individuals or items from a larger population for the purpose of conducting research. These methods are crucial in survey research, as they help ensure that the sample accurately represents the population, allowing for reliable and valid conclusions. The choice of sampling method can significantly influence the quality of the data collected and the insights gained from the study.
Telephone interviews: Telephone interviews are a method of data collection where respondents are contacted and questioned over the phone to gather information for research purposes. This approach allows researchers to reach a wide audience quickly, providing flexibility in scheduling and the potential for in-depth conversations, which can yield valuable insights.