Conducting a survey involves the systematic collection of data from a sample of a population, typically through methods like questionnaires, interviews, or observations. This process is a fundamental step in statistical inquiry, providing the raw data necessary to identify trends, patterns, and characteristics within a population, which can then be analyzed using various statistical techniques.

A graphic rating scale is a measurement tool that presents respondents with a line or continuum. Respondents mark their response at an appropriate point along this line, which often has descriptive anchors at the ends. This allows for a more nuanced measurement of attitudes or perceptions compared to simple categorical choices.

Opinion questions are designed to elicit the subjective views, beliefs, or attitudes of respondents regarding a specific topic. These questions are essential in social research for understanding how individuals perceive various issues or phenomena.

A questionnaire is a research instrument consisting of a set of questions or other types of prompts that aims to collect information from respondents about a specific issue, topic, or research objective.

Structured questions refer to the measurement level of the data (nominal, ordinal, interval, ratio). 'Non-disguised' refers to the purpose of the survey being clear to the respondent, which is a classification of the survey's intent rather than the mathematical structure of the question itself.

Sampling error refers to the difference between a sample statistic and the actual population parameter. This error arises because only a subset of the population is observed rather than the entire population. By increasing the sample size or using more efficient sampling designs, researchers can effectively reduce the sampling error, thereby increasing the reliability and precision of the estimates derived from the sample data.

The provided answer is 'A', though standard terminology usually refers to this as 'non-response bias'. The term 'responsive bias' is sometimes used in specific contexts to describe issues related to the nature of the responses received, but it is not the standard statistical term for missing data due to non-participation.

Sampling error arises from the fact that a sample is only a subset of the population. By increasing the sample size, the sample becomes more representative of the population, which reduces the standard error and provides more precise estimates of population parameters, thereby minimizing the sampling error.

Sampling error refers to the discrepancy between a sample statistic and the true population parameter, which occurs because only a subset of the population is observed. In a census, the entire population is measured, meaning there is no sampling process involved. Therefore, by definition, sampling error cannot occur in a census, although non-sampling errors may still persist.

Under-coverage bias occurs when some members of the population are inadequately represented in the sample. This happens if the sampling frame does not include all segments of the population, leading to results that may not generalize accurately to the whole group.