Types of Errors Affecting Research Design
Designing a research project requires time, skill, and knowledge. If you do not go through the process with clear intentions and methods, you may come up with distorted details or a distorted picture of what you are trying to achieve. With Qualtrics survey software, we simplify the process of creating a survey, but you may still feel frustrated with the scope of your research project.
This practical guide can help. While it is important to use the right method in research, it is equally important to avoid making serious mistakes that can result in negative results.
Some mistakes are made by asking the wrong questions:
- Population Specification Error
- Sampling Errors
- Selection Errors
- Frame Errors
- Survey Non-response Errors
Table of Content
- 1 Types of Errors Affecting Research Design
- 2 Errors Related to Improper Selection of Respondents
- 3 Errors Related to Accuracy of Responses
There are two general errors that may raise while the implications of the research designs.
- Improper selections of the Respondents
- Errors related to the accuracy of the responses
Population Specification Error
Definitions of human speculation occur when the researcher does not understand whom to examine. This can be tricky because there are many people who may eat the product, but only one who buys it, or they may lose the part they want to buy next time.
Example: Manufacturers of integrated goods often do research on housewives, because they are easy to contact, and it is thought that they decide what to buy and then make a real purchase. In this case, there is often a misinterpretation of the people. A man can buy an important share of the combined goods and have a visible direct and indirect influence on the purchase. For this reason, the absence of males in the samples may produce results aimed at the wrong audience.
Sample error is a mathematical error that occurs when the analyst does not select a sample that represents the total amount of data. As a result, the results obtained from the sample do not represent the results that would be available to the general population. Sampling is an analysis done by selecting a certain number of observations from most people. The selection method may specify sample errors as well as non-sample errors.
Sample error is a deviation from the sample value compared to the true population number. Sample errors occur because the sample is not representative of the population or is biased in any way. Even random samples will have some degree of sample error because the sample is the only human measurement from which it is derived. Sampling error can be reduced to some extent by designing a sample in a better way.
Some important methods that result in less sampling errors are discussed as follows:
- Increasing sample size: Increasing the sample size will reduce sampling errors. If the sample size is equal to the complete population, the scope of sampling error is zero.
- Stratification: It refers to dividing the given population into homogeneous and non-overlapping units or sub-groups (known as stratum) to make the sample more representative. Grouping is done on the basis of one or more common attributes. For example, there is a population of 1000 people, out of the entire population, 300 belong to high-class, 400 belong to middle-class, and 300 belong to low-class. The sizes of these strata are denoted by S1, S2 and S3.
When respondents choose to participate in the study and respond only to those who are interested, you may end up making a mistake because there may already be a natural bias. This may also be the case if participants do not comply with the study participants, or where there is bias in the way participants are grouped.
Example: Inquiries conducting a survey of shopkeepers have a natural tendency to choose the most responsive and acceptable respondents if there is the freedom to do so. Such samples usually consist of friends and acquaintances who have a certain degree of similarity in characteristics of those of the desired tribe.
How to Avoid This?
ticipate. The standard research process includes the start of a prestudy contact requesting collaboration, actual testing, and post-study follow-up. If no answer is found, a second request for a survey is made, perhaps via telephone interviews or in person.
Sample Frame Error – Frame error occurs when incorrect demographics are used to select a sample. The flaw in the old structure occurred in the 1936 presidential election between Roosevelt and Landon. The sample framework was based on vehicle registration and telephone references. In 1936, most Americans did not own a car or a telephone, and those who did have a large Republican population. The results mistakenly predicted the Republic’s victory.
Survey Non-response Errors
Non-response errors pertain to errors that occur due to differences between people that participate in surveys versus people who do not participate in surveys. Non-response errors happen when the respondents who complete the interview are somehow systematically different compared to those who were unable to be contacted and those who chose not to participate.
Example: In a telephone survey, some respondents are not available because they are not at home when the first call is made. Some have moved to or from home during the exam period. Non-respondents at home tend to be younger than younger children and have a much higher rate of working wives than home-based households.
People who moved or did not travel during the inspection period had significantly higher mobility than the average population. Therefore, many studies can anticipate errors in non-communication with respondents. Online surveys seek to avoid this error through email distribution, thus removing non-home responders.
How to Avoid This?
When collecting responses, make sure your first respondents participate and then use follow-up surveys and other access methods if they do not respond initially. You can also use various channels to reach your audience in person, in a web experiment, or via SMS.
This is the second type of errors while executing the research designing the following errors will raise generally.
Non-response errors happen owing to failure to garner thorough information on all units in the selected survey. Non-response error affect survey results in two ways. First, there is shrinkage in sample size or in the amount of information collected in response to a particular question results in larger standard errors.
Secondly, a prejudice is introduced to the degree that the distribution of some characteristics of non-respondents varies from the distribution of the respondents within a selected survey. Your respondents should accurately represent the number of people you want to emulate. If non-respondents are not evenly distributed to everyone, you will not have the right sample.
There are two ways in which an unanswered survey can take place:
- Contact (inability to communicate with all members of the sample framework); and
- Refusal (non-response to one or all of the items in the measuring instrument).
Respondents often represent positive or negative views on the topic of the survey and may not represent the target audience as a whole. Almost every survey contains some errors from non-access to the list of respondent representatives. For example, in a telephone study, non-respondents were not available because they were not at home when the first calls were made or were called, moved, or were not at home during the study period.
Surrogate Information Errors
In some research cases, the required information is not available. Instead, you can receive replacement data that will act as a representative of the required information. The need for replacement information arises from the inability or unwillingness of respondents to provide the requested information.
For example, decision-based behavioral research is always subject to behavioral predictions. This prevents many advertising research projects because one cannot see the future performance. Usually, researchers find one or more types of consensus information that are useful in predicting behavior.
Examples: You can get information about past behavior if you believe it reflects your behavior in the future. For example, if you want to market home computers in developing countries, you will research, among other things, educational levels, income, and home electricity. These changes affect the sale of home computers in developing countries errors related to the accuracy of the answers.
Measurement Errors From Interviewers
This error occurs in many areas throughout the research process, from the construction of your research to the analysis you found. Rating error can be revealed by the interviewer, the interrogator, or the respondent. Examples of measurement errors from the interviewer and questionnaire could include incorrect question names; bias in independent drawing objects; modification of informal discussion of question terms; misunderstanding or misinterpretation of the answer. On the respondent’s side, the rating error incorporates how the respondent interprets the question, and the respondent provides incorrect information.
Measurement Errors From Questions
Respondent error refers to any error included in the search results due to respondents providing incorrect or incorrect information. It is a form of formal bias. Several factors can lead to the error of the respondents. Linguistic and educational problems can lead to misunderstanding of the question by the respondent, or similarly, misunderstanding the examiner’s response. Remembering selections can lead to misinformation based on a poorly defined respondent.
Public bias may cause the respondent to respond in a way that he or she thinks is right or better or less embarrassing, rather than giving truthful and honest answers. In some cases, it may even be the case that some research is being done on an issue that is not publicly accepted, such as the use of paint as done by DuPont in 2001-02. Such a questionnaire may not be as effective as the Projective Technique. Therefore, some cases can be categorised where respondents may make a mistake by mistake.
Measurement Errors From Respondent
Measurement errors refer to those errors in the survey observations that may be committed by interviewers, respondents, data processors, and other survey personnel. At time, the reasons of measurement errors are poorly designed questions or questionnaire design, insufficient personal training or supervision, and inappropriate quality control.
Measurement errors are often underlying in the data and are only unravelled when the measurement process is repeated or responses are compared to error-free measurements. If repeated measurements are collated by the same measurement process, systematic errors may remain concealed. Several analytical techniques can be utilised to account for measurement errors in data analysis and inference, such as structural equation modelling instrumental variables and errors-in-variables modelling.