Validity and Reliability of Claims in Quantitative Research I have numbered the posts 1-2-3-4-5. Can you please write at least 100 words or little over relevant content to these posts in your with own words. If you find any research with references and citations, it will be great also.
These are discussion post, perhaps you do not need to answer each line just follow important context from the post to make comments, your own thoughts, own knowledge and probing question that move the conversation forward. (1) Reliability refers to the consistency or stability of test/measure scores.
Validity and Reliability of Claims in Quantitative Research Definition
Reliability means dependability or trustworthiness. In quantitative research, measures must be reliable in order to claim for the validity of the findings. All measures contain errors, so the more errors in the data, the less valid for claims or generalization. Reliability can be measured in several ways through correlation: Test-retest, Equivalent forms, Internal consistency, Intra-scorer, Inter-scorer reliability. Validity refers to the fact that the instrument actually measures what it claims to measure. The concept of validity is that the validity for whom or for what. Validity is only evaluated in terms of purpose and is categorized into several different types: content, construct, concurrent, and predictable validates.
(2) Data is only good if they are true reflections of what you are trying to measure. So making sure that the way you are collecting data helps with this
goal. For instance, if your method or your instruments or scales are unworthy or poorly designed, then others cannot really trust the data you have collected. So in addition to making sure that your data collection method is scientifically justifiable, you must also ensure that your instruments are both valid and reliable. You need to know the difference between these two terms. Valid means that the scale you are using is truly measuring what you purport to be measuring, and reliable means that the data collection will be true over time, so you compare the data collected over time, and if the scale is reliable, there will be no significant changes over time (that is why we collect multiple times to ensure that there are no significant changes.
(3) One of the aspects in looking at the outcomes of a research report is determining if the researcher analyzed the data accurately and did not make a Type
I or Type II error in their statistical analysis. In addition, looking at the source of the tools that were used in the research. Did the researcher explore
to ensure that the tool used in their research is based on a reliable and valid tool from previous research on the tool? This is another aspect of looking at
the reliability and validity of quantitative research. Any thoughts?
Validity and Reliability of Claims in Quantitative Research and Data Collection Methods
(4) The data collections tools validity is a great example of determining whether the data is reliable within a quantitative study. Without establishing
tools that will produce valid data the research is viewed more of an opinion of the population in question. For example, if I were to ask a neighborhood which ice cream was the best and base that data on the neighborhood as a whole my research is not valid. I do not have the support of my proposal that coffee flavored will be the best if my research is based solely on the opinions of my participants. I would need financial data stating which flavor is purchased more often, those who eat ice cream, age groups, season and so on.
(5) Quantitative research is research that uses numerical analysis. In essence, this approach reduces the data into numbers. The researcher knows in advance what he/she is looking for and all aspects of the study are carefully designed before the data is collected. The objective of quantitative research is to
develop and employ mathematical models, theories and/or hypotheses pertaining to phenomena. Validity is the extent to which an instrument measures what it is supposed to measure and performs as it is designed to perform. It is rare, if nearly impossible, that an instrument is 100% valid, so validity is generally measured in degrees. As a process, validation involves collecting and analyzing data to assess the accuracy of an instrument. There are numerous statistical tests and measures to assess the validity of quantitative instruments, which generally involves pilot testing.
