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Research Methods

This libguide provides tips on how to choose research methods, whether it be quantitative, qualitative, or mixed methods. It highlights the strengths and limitations of each method, and provides guidance on how to identify the appropriate method for a giv

Quantitative Research Methods

Quantitative research methods involve the collection of numerical data and the use of statistical analysis to draw conclusions. This method is suitable for research questions that aim to measure the relationship between variables, test hypotheses, and make predictions. Here are some tips for choosing quantitative research methods:

Identify the research question: Determine whether your research question is best answered by collecting numerical data. Quantitative research is ideal for research questions that can be quantified, such as questions that ask how much, how many, or how often.

Choose the appropriate data collection methods: Select data collection methods that allow you to collect numerical data, such as surveys, experiments, or observational studies. Surveys involve asking participants to respond to a set of standardized questions, while experiments involve manipulating variables to determine their effect on an outcome. Observational studies involve observing and recording behaviors or events in a natural setting.

When choosing a data collection method, it's important to consider the feasibility, reliability, and validity of the method. Feasibility refers to whether the method is practical and achievable within the available resources, while reliability refers to the consistency of the results over time and across different observers or settings. Validity refers to whether the method accurately measures what it's intended to measure.

The sample size: Decide on the sample size that is needed to produce statistically significant results. The sample size is the number of participants in the study. The larger the sample size, the more reliable the results are likely to be. However, a larger sample size also requires more resources and time. Therefore, it's important to determine the appropriate sample size based on the research question and available resources.

Statistical test: Choose the appropriate statistical analysis techniques based on the type of data you have collected and the research question. Common statistical analysis techniques include descriptive statistics, correlation analysis, regression analysis, and t-tests. Descriptive statistics summarize the data using measures such as mean, standard deviation, and frequency. Correlation analysis examines the relationship between two or more variables. Regression analysis examines the relationship between one dependent variable and one or more independent variables. T-tests compare the means of two groups.

When selecting a statistical analysis technique, it's important to consider the assumptions of the technique and whether they are appropriate for the data being analyzed. It's also important to consider the level of statistical significance required to draw meaningful conclusions.

Strength and Limitations

Strength and Limitations of Quantitative Research Methods:


  • The use of statistical analysis allows for the identification of patterns and relationships between variables.
  • Provides a structured and standardized approach to data collection, allowing for replication of studies and comparisons across studies.
  • It can produce reliable and valid results which are generalizable to larger populations.
  • Allows for hypothesis testing, making it suitable for research questions that require a cause-and-effect relationship.
  • It can produce numerical data, making it easy to summarize and communicate results.


  • It may oversimplify complex phenomena by reducing them to numerical data.
  • It may not capture the context and subjective experiences of individuals.
  • It may not allow for the exploration of new ideas or unexpected findings.
  • It may be influenced by researcher bias or the use of inappropriate statistical techniques.
  • It may not account for variables that are difficult to measure or control.