Q3: Discuss validity and reliability in quantitative research.
Discuss validity and reliability in quantitative research.
Introduction
In quantitative research, validity and reliability are two concepts that are often conflated. Validity refers to whether a measurement is accurate or reliable; it’s the relationship between your measure and the phenomenon you’re trying to measure. Reliability refers to consistency in repeated measurements of the same thing; it’s also known as test-retest reliability because it applies when people take tests on different occasions. The strongest evidence for the validity of a measure is when levels of the construct you are trying to measure show strong correlations over time, such as in longitudinal studies.
Validity is concerned with whether the measurement is correct.
Validity is concerned with whether the measurement is correct. A valid measure will have high content-related validity and low criterion-related validity, while a poor-quality or invalid measure will have low content-related validity but high criterion-related validity.
The degree to which a measure measures what it is supposed to measure (content) and/or how reliable it is (reliability) can be measured by looking at how closely your instrument matches up with its intended purpose. For example, if you were trying to determine the number of jelly beans in an assortment jar and came across one with only two left over from when I bought them last month–and knowing that those two were not enough for me–you would conclude that my guess about how many jelly beans there should be based on past experience was not very accurate! Similarly, if we had been asked how many jelly beans were left after our last purchase but we had no idea ourselves because someone else counted them out for us before we left their house…well…that would’ve been pretty embarrassing too! In these cases though both are examples where something went wrong because they weren’t measuring what they should have been measuring (i
Reliability measures how consistent an observed pattern is across repeated measurements.
Reliability measures how consistent an observed pattern is across repeated measurements. It can be calculated for any measure, from the amount of time it takes you to get ready in the morning to how many times you’ve been late for work this week.
Reliability is important for valid and reliable data because it ensures that each measurement has a similar meaning. If someone measures your height at two different times, they need to make sure that they were using the same tape measure or ruler at both times; otherwise, there would be no way of knowing whether one person’s reading was more accurate than another’s (and what good would knowing this?). In addition, reliability helps ensure that we can generalize our findings beyond individuals who gave us their best effort when completing their particular questionnaire or test–if only one participant in our study gave his/her best effort on every question but still ended up getting incorrect answers due to pressure from classmates or friends who encouraged him/her not too do well on certain questions (for example), then we won’t know whether or not those answers reflect his/her true feelings about those topics instead being influenced by peer pressure alone…
The strongest evidence for the validity of a measure is when levels of the construct you are trying to measure show strong correlations over time, such as in longitudinal studies.
Validity is the degree to which a measure is capable of measuring what it is supposed to measure. Reliability is the degree to which a measure is consistent over time, such as in longitudinal studies.
Validity refers to whether your results are based on valid evidence and not just guesswork or assumptions that may be incorrect (e.g., if you choose people from different neighborhoods). Reliability refers more specifically to whether your data show a high degree of stability over time so that any changes you see in them can be attributed directly back onto changes in other variables involved with those variables (e.g., participants’ health).
If you want to be able to generalize from your results, it’s important that your measures are reliable and valid.
One of the most important things about any research project is generalization. Generalization means that you can apply your results to other situations, similar or dissimilar. For example, if you find that people in your study tend to have positive feelings about their relationships with others and their career advancement opportunities, then this could be useful information for future studies–you might want to recommend that everyone else does the same thing!
Reliability and validity are two important factors for ensuring generalization: reliability refers to consistency over time; validity refers to how well a measure actually measures what it’s supposed to measure (more on this later).
You can’t get valid or reliable data unless you start out with a valid and reliable measure.
You can’t get valid or reliable data unless you start out with a valid and reliable measure. Let’s take an example. Suppose that you’re interested in whether people are satisfied with their lives, so you’re going to ask them questions about their satisfaction levels. If these questions aren’t answered honestly, then your results won’t be accurate: they may give an overly positive impression of how happy people are (or not). Additionally, they may be answered incorrectly by some respondents who want to appear happier than they actually are–this would skew the results toward overreporting happiness.
So here’s what we need: a well-designed survey instrument that measures true happiness levels (i.e., measures something important) while also being reliable enough for us to accurately interpret those answers as such (i.e., consistent across many different people). This is why we use reliability coefficients instead of just raw scores–they measure consistency over time rather than just one single measurement point within our research project!
Conclusion
Validity and reliability are two important concepts in quantitative research. Validity is concerned with whether the measurement is correct, whereas reliability measures how consistent an observed pattern is across repeated measurements. The strongest evidence for the validity of a measure is when levels of the construct you are trying to measure show strong correlations over time, such as in longitudinal studies. If you want to be able to generalize from your results, it’s important that your measures are reliable and valid