• Introduction

RSCH 7864 Assessment 4 Quantitative research involves collecting and analyzing numerical data to identify patterns, relationships and trends. It is widely used to support evidence -based decision -making in the social sciences, health care, business and other fields. This assessment examines quantitative research design, data collection techniques, statistical analysis methods and moral ideas.

Key Components of Quantitative Research

1. Research Design

• Quantitative research follows the structured methodology to ensure reliability and validity:
• Descriptive research: Overview and describes the properties of population or event.
• Prison care: Checking the relationship between variables without establishing the cause.
• Experimental research: Causes and influences use controlled experiments to test conditions.
• Semi-practical research: Similar to experimental research, but lack of random tasks by participants.

2. Data Collection Techniques

• Quantitative research depends on standardized data collection methods:
• Examination and questionnaire: Collect structured answers from the participants.
• Use: Measure the effect of variables under controlled conditions.
• Overview studies: Systematically records behavior and pattern.
• Secondary Data Analysis: Uses existing dataset for new research insight.

3. Sampling Strategies

• The committee ensures representation of the study population:
• Random sampling: Each person has a similar chance of choice.
• Striped sampling: The population divides the population into subgroups before the sample.
• Systematic sampling: Selects each NTH participant from the population list.
• Sampling of systems: Uses easily accessible participants (less stiff but practical).

Statistical Analysis Methods

1. Descriptive Statistics

• Descriptive characters summarize and present data effectively:
• Media, means and mode: Measures for central trend.
• Standard deviation and variance: Measurements of data spread.
• Frequency distribution: represents data events.

2. Inferential Statistics

• Subordinate characters help draw conclusions beyond computer tests:
• T-TEST: Compare between two groups.
• Anova (variance analysis): Comparison is in many groups.
• Recovery analysis: Determines the ratio of independent and dependent variables.
• Chi-Square Test: Checking the ratio of the classified variables.

3. Data Visualization

• Quantitative research results are often presented:
• Graff and diagrams: bar graph, line graph, cake diagram and spread plot.
• Tables and infographics: Brief representation of numerical data.

Case Study: The Impact of Online Learning on Student Performance

• A quantitative study examined the effect of online learning on the student’s academic performance during the COVID-19 epidemic.
• Research design and features:
• Example size: 500 students from different educational institutions.
• Data collection: Students on online surveys to measure engagement and performance.
• Data analysis: Realization analysis to evaluate the relationship between the online learning time and grades.

Findings:

• Students who spent more than 10 hours per week on online learning showed 15% improvement in the grade.
• The challenges included distracted, technical problems and lack of instructor negotiations.
• The hybrid learning model achieved the highest student satisfaction rates.

Conclusion:

Academic results are positively affected when they are effectively structured from learning online, but challenges should be solved to adapt the results.

Ethical Considerations in Quantitative Research

• Informed consent: Participants shall be aware of the purpose of the study and agree to participate voluntarily.
• Privacy: Researchers must protect the individual data and privacy of participants.
• Data integrity: To ensure accurate and honest data collection and reporting.
• Reducing prejudice: randomization, blending and proper sampling technology helps to reduce prejudice in resea