Related Experiment Video
Updated: May 5, 2026
07:29
Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters
Published on: November 22, 2019
7.5K
Core reporting expectations for quantitative manuscripts using independent and dependent t-tests, One-Way ANOVA, OLS
James B Schreiber1, Mohamed Ezzat Khamis Amin2
1Duquense University, United States.
Currents in Pharmacy Teaching & Learning
|September 13, 2025
Summary
This guide details reporting standards for quantitative analyses like t-tests, ANOVA, regression, and Chi-Square. It provides checklists and examples for clear scientific communication in pharmacy education.
Area of Science:
- Pharmacy Education
- Quantitative Methodology
Background:
- Journalistic standards for reporting quantitative research are crucial for clarity.
- Methodological rigor ensures the validity and reproducibility of scientific findings.
Purpose of the Study:
- To outline expectations for reporting quantitative designs and analyses in Currents in Pharmacy Teaching and Learning.
- To provide a methodological resource for researchers in pharmacy education.
Main Methods:
- Overview of common statistical tests: independent and dependent t-tests, One-Way ANOVA, linear regression, and Chi-Square.
- Inclusion of checklists for each analysis to guide reporting.
- Provision of example narratives for each statistical method.
Main Results:
- The article offers a foundational understanding of selected quantitative analyses.
- Checklists and example narratives serve as practical tools for authors.
- The content is tailored for the specific needs of the journal and its readership.
Conclusions:
- Adherence to these reporting guidelines will enhance the quality and clarity of quantitative research published in the journal.
- This resource aims to improve methodological consistency and understanding among pharmacy educators and researchers.
Keywords:
Data analysis reporting
Related Concept Videos
02:46Chi-square Analysis
43.6K
The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
43.6K
01:16Hypothesis Test for Test of Independence
7.4K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
H0: The two variables (factors)...
7.4K
01:23Test for Homogeneity
2.4K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
2.4K
01:18One-Way ANOVA
11.9K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
11.9K
01:12Statistical Methods to Analyze Parametric Data: ANOVA
1.5K
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
1.5K
01:09Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test
5.5K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
5.5K