• Instrument that will be used to address your research question. (Electronic questionnaires). In addition to the research variables, the questionnaire will collect demographic information (gender, age, level of education, and race)
• The instrument selected and the level of measurement •
Description of the type of scale used in the instrument.
• Extraneous variables and how to control them
SOLUTIONS
- Description of the type of scale used in the instrument:
The type of scale used in an instrument refers to the way in which data is measured or quantified. The most commonly used types of scales are nominal, ordinal, interval, and ratio scales.
- Nominal scale: This type of scale is used for data that is non-numerical and cannot be ranked, such as categories or labels. Examples of nominal scales include gender, race, and political party affiliation.
- Ordinal scale: This type of scale is used for data that can be ranked, but the differences between the rankings are not necessarily equal. Examples of ordinal scales include rankings of customer satisfaction or academic achievement.
- Interval scale: This type of scale is used for data that can be ranked and the differences between the rankings are equal, but there is no true zero point. Examples of interval scales include temperature (in Celsius or Fahrenheit) and IQ scores.
- Ratio scale: This type of scale is used for data that can be ranked, the differences between the rankings are equal, and there is a true zero point. Examples of ratio scales include weight, height, and income.
The choice of scale used in an instrument depends on the type of data being collected and the research question being investigated. For example, if the research question involves measuring the effect of a drug on blood pressure, a ratio scale would be appropriate to use for blood pressure measurements.
- Extraneous variables and how to control them:
Extraneous variables are any variables that may influence the outcome of a study but are not the variables of interest. These variables can confound the results of a study and make it difficult to determine causality. Therefore, it is important to control for extraneous variables to ensure that the results of a study are valid and reliable.
There are several ways to control for extraneous variables:
- Randomization: Random assignment of participants to different groups can help to balance extraneous variables across the groups.
- Matching: Participants can be matched on certain variables to ensure that each group has similar characteristics.
- Statistical analysis: Statistical techniques such as regression analysis can be used to control for the effects of extraneous variables.
- Manipulation: Manipulating the extraneous variable can help to isolate the effects of the variable of interest.
Controlling for extraneous variables can be challenging, but it is an important part of research design to ensure that the results are accurate and meaningful.
