Saturday, January 31, 2009

week 4 measurement

Blog Question: What distinguishes Quantitative from Qualitative designs,
what is the difference between “validity” and “reliability,” and what is
meant by the terms “probability” and “significance?”

According to Goubil-Gambrell, quantitative research tries to attach numerical values to variables, populations, samples, etc. and show their relationship to each other. When conducting quantitative inquiry, researchers perform experiments that manipulate the variables for analysis in order to establish causal links. Qualitative research is descriptive and identifies variables in light of circumstances that frame inquiry. Lauer and Ashe add that qualitative research is not concerned with treatments or manipulating control groups.

Frederick Williams offers an analysis of validity and reliability and provides key distinctions. The main difference is that validity requires some standard outside of the component of measurement to which comparisons are made, whereas reliabiltiy is concerned with "comparing a measure with itself," but one does not necessarily imply the other. In other words, validity questions the fitness of a researchers tools to that thing he/she claims to measure and reliability has to do with the replication of research, which may or may not be consistent based on the component "subparts."

Probability means that an outcome will occur in all likelihood due to the necessary factors being in place, whereas significance occurs based on circumstances that are not necessarily present. In the words of Lauer and Ashe, "significance in statistics is a statement of the degree of rarity of a result based on chance probability alone. In almost all cases of statistical analysis, the calculated statistic is compared with standard, tabled values of chance distribution of that statitistic. If the calculated value is sufficiently larger (for almost all statistics) than the tabled value, the result is called statistically significant, meaning that the statistical relationship between two variables observed is unlikely to have occurred simply by random chance alone. Significance is declared usually at a probability (p) level of five or one percent" (287, emphasis mine).


  1. I enjoyed reading your post, Nicole. In particular I think that your definitions are right on the money. And you provide source references to support your definition. Nicely done.

    Hopefully you can help me with a very fundamental question. This is not specific to anything you've said or not said; mainly, it relates to a personal struggle with the material.

    Simply put...what does this do for us? The most immediate answer is that this language helps us to converse with each other (and, perhaps, other researchers). I suppose that one could argue that this orientation to measurement will aid us in our own research designs. I agree with both conclusions.

    However, my goal is to complete research. Having read this material, I cannot claim the ability to do more than define these terms. I'm not sure I can see (or practice) these things "in the flesh" so to speak.

    For examples, how do we evaluate validity? Reliability? When is the right distribution or statistic in place? Based on these, can we now diagnose or practice what we preach?

    I'm not sure. Perhaps I'm too impatient for my own good :).

    Perhaps you can help in my problem. Where do we go to learn how to "do" as opposed to "define" this material?

  2. luckily for me, glen, i'm NOT being called to do at this very moment and only am being asked to define. i, too, worry about attempting to quantify through research design, especially in the discipline of rhetoric and writing studies as a general field. I mean, how do you apply numerical measurements to units of sentence, paragraph, etc? writing is epistemic and reflects knowledge in its state of ever unfolding. measurement. schmeasurement.

  3. p.s. that was me who actually logged on to my blog as "MyStro."