A quantitative hypothesis contains a null and an alternative proposition that is either proved or disproved through statistical analysis. The process speculates that an independent variable affects a dependent variable and an experiment is conducted to see if there is a relationship between the two. This type of hypothesis is stated in numerical terms and has specific rules and limits. The null hypothesis is either rejected or accepted as a result of statistical data gathered during a set of experiments.
One of the main differences between a qualitative and a quantitative hypothesis is that it has very specific limits. An example of a null hypothesis might be "five additional hours of study time per week lead to a higher grade point average in college students." The alternative hypothesis would probably state "five additional hours of study time per week does not increase the grade point average of college students." In order to reject or accept the null hypothesis, experimental data would need to be recorded over a specified period of time.
Most studies that set out to test a quantitative hypothesis measure data based on statistical significance, which means there is a low possibility of error. In the case of proving or disproving the effect of study time on the grade point averages of college students, a control group would most likely be tested. The behaviors and environments of these groups are usually controlled by the researchers. Data would also be obtained from a group of students whose behaviors and environments were not controlled.
Since a quantitative hypothesis and research study rely on numerical data, the results of an experiment or surveys are translated into mathematical values. For example, many market research studies use scales that assign a numerical value to each response. A reply of "agree" may correspond to the number "4," while a response of "disagree" may correspond to the number "2." When all of the survey feedback is recorded and analyzed, a percentage based on the total amount of responses is then assigned to each number.
Statistical analysis is often used to examine the results of survey and experimental data. Whether the quantitative hypothesis is rejected or accepted is dependent on the numerical result of the analysis. For example, if the average grade point average must be at least 3.5 in order to prove that the amount of study time has a direct effect, an average of 3.45 would result in a rejection of the quantitative hypothesis.
GhostPug Post 2 |
I find it helpful to define and understand concepts like quantitative hypothesis by also looking at the opposite term; here, that would be qualitative research.
Fundamentally, a quantitative hypothesis is a statistical, numerical, objective examination of cause and effect. Research begins with the hypothesis, gathers and examines data, and then evaluates the validity of the hypothesis with the data gathered. In the article, the question "Does increasing study time affect student grades?" is asked, and using the quantitative approach, it can be answered and the results can be generalized across multiple populations.
Qualitative research is an explanatory, narrative, and subjective examination of observed and reported behavior. Qualitative research would begin by noting that students who stayed after class with their professor scored higher an their exams, and would possibly conclude that the higher scores and time spent with the professor correlated with a deeper interest with the material itself. |