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Designing a research project takes time, skill and knowledge. With Qualtrics survey software, we make the survey creation process easier, but still you may feel overwhelmed with the scope of your research project.
Here are 5 common errors in the research process.
1. Population Specification
This type of error occurs when the researcher selects an inappropriate population or universe from which to obtain data.
Example: Packaged goods manufacturers often conduct surveys of housewives, because they are easier to contact, and it is assumed they decide what is to be purchased and also do the actual purchasing. In this situation there often is population specification error. The husband may purchase a significant share of the packaged goods, and have significant direct and indirect influence over what is bought. For this reason, excluding husbands from samples may yield results targeted to the wrong audience.
2. Sampling
Sampling error occurs when a probability sampling method is used to select a sample, but the resulting sample is not representative of the population concern. Unfortunately, some element of sampling error is unavoidable. This is accounted for in confidence intervals, assuming a probability sampling method is used.
Example: Suppose that we collected a random sample of 500 people from the general U.S. adult population to gauge their entertainment preferences. Then, upon analysis, found it to be composed of 70% females. This sample would not be representative of the general adult population and would influence the data. The entertainment preferences of females would hold more weight, preventing accurate extrapolation to the US general adult population. Sampling error is affected by the homogeneity of the population being studied and sampled from and by the size of the sample.
3. Selection
Selection error is the sampling error for a sample selected by a nonprobability method.
Example: Interviewers conducting a mall intercept study have a natural tendency to select those respondents who are the most accessible and agreeable whenever there is latitude to do so. Such samples often comprise friends and associates who bear some degree of resemblance in characteristics to those of the desired population.
4. Non-responsive
Nonresponse error can exist when an obtained sample differs from the original selected sample.
Example: In telephone surveys, some respondents are inaccessible because they are not at home for the initial call or call-backs. Others have moved or are away from home for the period of the survey. Not-at-home respondents are typically younger with no small children, and have a much higher proportion of working wives than households with someone at home. People who have moved or are away for the survey period have a higher geographic mobility than the average of the population. Thus, most surveys can anticipate errors from non-contact of respondents. Online surveys seek to avoid this error through e-mail distribution, thus eliminating not-at-home respondents.
5. Measurement
Measurement error is generated by the measurement process itself, and represents the difference between the information generated and the information wanted by the researcher.
Example: A retail store would like to assess customer feedback from at-the-counter purchases. The survey is developed but fails to target those who purchase in the store. Instead, results are skewed by customers who bought items online.
While measurement error may be difficult to measure accurately it can be minimized by: • Careful selection of the time the survey is conducted;
• using an up-to-date, accurate sample framework;
• revisiting or conducting 'call backs' to unavailable respondents;
• Careful questionnaire design;
• Providing thorough training for interviewers and processing staff; and
•being aware of all the factors affecting the topic under investigation