Questionnaire and Survey Design
Course code
RAS6032.YK
old course code
Course title in Estonian
Küsitlusmeetodid
Course title in English
Questionnaire and Survey Design
ECTS credits
6.0
Assessment form
Examination
lecturer of 2023/2024 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
lecturer of 2024/2025 Autumn semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Course aims
To create opportunities for the formation of knowledge about the methods and techniques of conducting a formalized sociological survey.
Brief description of the course
Survey capabilities and limitations. Sample and population. Representativeness. The size of the sample, its relation to the dispersion of the measurement results, the accuracy of the measurement and the probability of error. Finding the sample size for a given measurement error, explaining the reliability of the difference in measurement results based on the sample size.
Sampling errors. Sample weighting. Probability and non-probability sampling. Finding interviewees. Fixing and transforming the dataset, applying weight to the dataset, replacing a missing value in the dataset.
Requirements and possible errors for the preparation of questions. Formal and substantive types of questionnaires. Structure of the questionnaire. Program and test questions. Possibilities of recording answers, types of scales, data analysis requirements. Continuous and discrete features, scale informativeness. Neutral response, no response, substitution options. Measuring duration and frequency. Other survey techniques. Semantic differential and reliability of bipolar scales. Sociometry. Online surveys.
Multi-question metrics.
Metric validity. Measurement reliability, Cronbach's alpha.
Forming an amount identifier from different types of identifiers, forming a counting identifier. Factor analysis and factor characteristic, cluster analysis and cluster characteristic.
Interpretation of survey results and survey report.
During the course, you will familiarize yourself with questionnaires and research reports prepared on various topics and in various forms.
In the practicals, you will learn to use the data analysis program R.
As an independent exercise, a questionnaire with a predetermined content and volume and a test of the use of the R program must be prepared.
Sampling errors. Sample weighting. Probability and non-probability sampling. Finding interviewees. Fixing and transforming the dataset, applying weight to the dataset, replacing a missing value in the dataset.
Requirements and possible errors for the preparation of questions. Formal and substantive types of questionnaires. Structure of the questionnaire. Program and test questions. Possibilities of recording answers, types of scales, data analysis requirements. Continuous and discrete features, scale informativeness. Neutral response, no response, substitution options. Measuring duration and frequency. Other survey techniques. Semantic differential and reliability of bipolar scales. Sociometry. Online surveys.
Multi-question metrics.
Metric validity. Measurement reliability, Cronbach's alpha.
Forming an amount identifier from different types of identifiers, forming a counting identifier. Factor analysis and factor characteristic, cluster analysis and cluster characteristic.
Interpretation of survey results and survey report.
During the course, you will familiarize yourself with questionnaires and research reports prepared on various topics and in various forms.
In the practicals, you will learn to use the data analysis program R.
As an independent exercise, a questionnaire with a predetermined content and volume and a test of the use of the R program must be prepared.
Learning outcomes in the course
Upon completing the course the student:
- knows the concepts related to the selection of interviewees and different questionnaires;
- knows the methods of sampling and questionnaire preparation,;
- knows how to perform calculations relating the sample size and measurement errors;
- knows how to prepare simpler questionnaires;
- knows and knows how to use certain options for the correction, transformation and analysis of survey data using a computer (R program);
- can present and make sense of survey data.
- knows the concepts related to the selection of interviewees and different questionnaires;
- knows the methods of sampling and questionnaire preparation,;
- knows how to perform calculations relating the sample size and measurement errors;
- knows how to prepare simpler questionnaires;
- knows and knows how to use certain options for the correction, transformation and analysis of survey data using a computer (R program);
- can present and make sense of survey data.
Teacher
Jüri Kruusvall
Prerequisite course 1
Study programmes containing that course