Introduction

In a study, hypothetical design is based on the formulation of problems and hypothetical investigations. For a quantitative study, the hypothesis used is measurable, i.e., the hypothesis must be tested using realistic criteria. It is not necessary to use the evolutionary principles of self-examination.

The size and inference of the study depend on the statistical data, and the results of the study are depicted in the hypotheses. For example, many statistical studies use metrics that assign a numeric value to each correlation. The answer to ” agree” may be related to the number “4”, while the answer to “disagree may be related to the number “2”. When the entire study entry is analyzed and segregated, a rate is assigned to each number based on the total number of interactions. Statistical tests are usually used to analyze the results of abstract and test data. The number of trials depends on the result, whether the assumption of size is rejected or accepted.

Types of Quantitative Hypothesis

In general, quantitative hypotheses fall into three categories: descriptive hypothesis, comparative hypothesis, and associative hypothesis (Bagikan, 2015).

Descriptive Hypothesis

These types of hypotheses are temporary assumptions about the value of a variable that has nothing to do with relationships or expressions. Statistics used to test descriptive hypotheses are tests of mean or constant deviation (Bagikan, 2015). An expert plans a hypothesis based on an extension of the problem and a hypothetical study. Here are some examples of problem formulations (PF) and hypotheses (H).

For example:

PF: What is the level of dominance of science in the college course?
H: The dominance of college science teachers in this area is up to 70%.
PF: To what extent does Class 12 dominate the Class 12 subject?
H: The dominance of class eleven by class ten reaches 75%

Comparative hypothesis

This hypothesis is a temporary statement that analyzes the values of two variables. In this hypothesis, the researcher does not determine the value of the variables they see but instead considers them (Bagikan, 2015). In other words, the two factors are very similar, but there are different examples. The measurements used to test this hypothesis use the t-test (which is expected to respond to homogeneity and normality.

For Example:

PF: Is there a difference in the problem-solving skills of advanced students compared to Y learners?
H: Difficulty in bringing students’ ability to improve themselves from learning.
PF: Are there contradictions in the basic thinking skills of day students?
H: There is no difference between students’ basic skills studying in the early morning and students studying in the evening.

The above two hypotheses are somewhat different. In the first hypothesis, the researchers affirm the ability to improve student’s ability rather than the students learning Y. In the second hypothesis, the claim is not one-sided because students’ critical thinking skills during the day are better or worse. The hypothesis only reveals the existence of contradictions. The first hypothesis is the one-sided test hypothesis, and the following hypothesis is called the two-sided test hypothesis.

Associative hypothesis

This hypothesis builds the relationship between two factors: the dependent and independent variables (Bagikan, 2015). The statistics are used to test this hypothesis using Product Moment, Double and Partial Correlation.

For example:

PF: Is there a relationship between student success and student stress?
H: There is an inverse relationship between student achievement and student stress level.
PF: Is there a relationship between student learning outcomes and guest performances?
H: There is a positive relationship between student achievement and student stress.

The first hypotheses contain the phrase “negative relationship.” The negative relationship is the opposite. It assumes that students have high levels of stress and low academic performance. The second paradigm is the phrase “positive relationship.” A positive relationship means a face-to-face meeting. It means that learning outcomes for students will be better if the guest program is acceptable.

If- then structure of hypothesis:

In a statement revealing the exact relationship between the independent and dependent variables, it is natural to consider the hypothesis as IF-THEN is structured. Since the independent variable affects the dependent variable, IF-THEN expression can help to communicate this relationship hypothesis (Helmenstine, 2020). For example, if the researchers consider the previous example of the child labour hypothesis, the researchers can say: “Assuming more school dropouts, the child labour rate increases. It is a clear indication that dropout affects the rate of child labour.

Null and alternative hypothesis:

In a quantitative study, the global evolutionary hypothesis has two structures, the null hypothesis (Ho) and the alternative hypothesis (Ha). A quantitative hypothesis is a null and alternative hypothesis that has been proven or rejected by a survey. The hypothetical design assumes that the independent variable affects a dependent variable and tests whether there is a correlation between the two (Zeng, Pan & Lin, 2020). This type of hypothesis is expressed in terms of numbers and has clear guidelines and breakpoints. The null hypothesis is rejected or approved due to statistical data collected during the experiments. An example of a null hypothesis is that “the highest GPA for undergraduate students with an extra five hours of long-term study per week is a preferred assumption.

Positive and negative association:

These relationships are usually presented as follows: Positive Connection (+): It is assumed that at least two variables are systematically correlated when changing similar paths. In this way, if one variable does not increase, the other increases additionally, or if one decreases, the other decreases in the same way (Yusup, Naufal and Hardini, 2018). For example, as students’ stress scores are increased, discrepancies in their shots create more fingerprints. Negative connection (-): It is assumed that at least two variables are inversely related to each other when switching to opposite bearings.

Significance of Quantitative hypothesis design:

Theories are generally derived from hypotheses and can be established based on research conducted in this regard. Hypothesis development is closely related to the written examination conducted for the study (Meyer, Van Witteloostuijn & Beugelsdijk, 2017).

For example, in a study on child labour, the research question is: What is the relationship between the rate of dropout in primary school and the rate of child labour? When dropout rates increase, in primary education, the rate of child labour among children between the ages of 6 and 10 also increases. Theories typically include an independent variable, a bias variable, the expected effect, and the population under study. In the example above, education is a free variable, and the variation depends on child labour and people aged 6-10. As a result, child labour is expected to increase as enrollment increases. Most theories show the effect of the relationship between the dependent and independent variables.

Conclusion

This assessment reveals the hypothetical quantitative method, designs the exact relationship between the independent and dependent variables, and it is natural to consider the hypothesis a sit structured based on researchers’ assumptions and experimental values. Since the independent variable affects the dependent variable, the hypothesis can help to communicate between these variables. Thus, a hypothesis can be viewed as a suggested (or speculative) alternative to a research question. In quantitative studies, the hypothesis is expressed before the study and then tested during the study.

References

Bagikan, S. (2015). Definitions and Examples of Hypothesis. Education Articles. Retrieved July 10, 2021, from http://prasko17.blogspot.com/2015/03/definitions-and-examples-of-hypothesis.html.
Helmenstine, A. M. (2020). Do You Know What Makes a Good Hypothesis? Here’s Some Examples. ThoughtCo. Retrieved July 10, 2021, from https://www.thoughtco.com/examples-of-a-hypothesis-609090.
Meyer, K., van Witteloostuijn, A., & Beugelsdijk, S. (2017). What’s in a p? Reassessing best practices for conducting and reporting hypothesis-testing research. Journal Of International Business Studies, 48(5), 535-551. https://doi.org/10.1057/s41267-017-0078-8
Yusup, M., Naufal, R. S., & Hardini, M. (2018). Management of utilizing data analysis and hypothesis testing in improving the quality of research reports. Artist Transactions on Management (ATM), 2(2), 159-167.
Zeng, D., Pan, Z., & Lin, D. Y. (2020). Design and analysis of bridging studies with prior probabilities on the null and alternative hypotheses. Biometrics, 76(1), 224-234.