Five Good Reasons for Doing Quantitative Research

Five Good Reasons for Doing Quantitative Research

Maria T Ping & Willy A Renandya

 “Numbers don’t lie” is the title of a song by an American pop band, the Mynabirds. One stanza of the lyric is telling:

You said, “One and one and one and one is three.”
But I know my lines and my graphs and my math-
Honey, that ain’t me.

We know that 1+1+1+1 is four, not three. It is not mathematically correct to say that the total number is 3. Numbers present an objective, verifiable piece of information. If your weight was 60 kg last year and this year it is 70, you have gained 10 kg in the span of 12 months.

Whether an increase of 10 kg is considered okay, just nice, shocking, terriying, etc is a matter of interpretation. The number however remains the same: 10 kg. Like they say, numbers don’t lie (but people may)!!

Here are some good reasons why a quantitative method may be useful for your research and classroom practices.

1. When you collect language test data, you may want to report the test scores quantitatively using common statistics such as mean, median and standard deviation. Sometimes you may want to present test performance in terms of percentages, e.g., 65% of the students performed below the mean (average). Descriptive statistics such as these help summarize quantitative information in a very efficient manner

2. When you want to summarize past research studies, you probably need to employ a quantitative method known as meta-analysis. One statistical procedure often used in this type of research is effect size, expressed in terms of Cohen’s d.

3. When you want to find out how two or more variables are correlated (e.g., whether vocabulary size is correlated with students’ speaking or writing skills).

4. When you want to compare two or more groups, or two or more sets of scores in order to draw a conclusion about the effectiveness of a classroom-based intervention (e.g. teaching strategies, teaching materials, etc.). Statistical procedures such as the t statistics or Anova (Analysis of Variance) help you find out whether there are significant differences between those sets of scores.

5. When you need to display ‘’big, more objectively rigorous data’’ to support your more open and flexible ‘’thick data’’ (qualitative explanation).

There are many other reasons, but the five reasons above are just illustrative examples of why and how quantitative methods may be what you need to analyze your data.

Further reading

Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2011). How to design and evaluate research in education (10th edition). New York: McGraw-Hill Humanities/Social Sciences/Languages.


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