4 types of quantitative research

• Purpose: Experimental research is considered the gold standard for establishing cause-and-effect relationships. It involves manipulating one or more independent variables to determine their effect on a dependent variable while controlling for other extraneous factors. The key characteristic of true experiments is the random assignment of participants to different treatment conditions.  
• Methods: True experiments typically involve:
  • Manipulation: The researcher actively changes the levels of the independent variable.
  • Control: The researcher uses a control group that does not receive the treatment or receives a standard treatment for comparison.
  • Random Assignment: Participants are randomly assigned to the different treatment groups to ensure that groups are as similar as possible at the start of the experiment, minimizing the influence of confounding variables.
• Data Analysis: Data is analyzed using inferential statistics such as t-tests, ANOVA, and factorial designs to determine if there are statistically significant differences between the groups on the dependent variable.
• Example: A researcher might randomly assign participants to either a new teaching method or a traditional teaching method and then compare their performance on a standardized test to determine if the new method causes improved learning outcomes. Another example could be testing the effectiveness of a new drug by randomly assigning patients to receive either the drug or a placebo and then comparing their recovery rates.

Understanding these four types of quantitative research is fundamental to designing and interpreting research studies. The choice of which type to use depends heavily on the research question, the nature of the variables being studied, and the feasibility of manipulating and controlling those variables. Remember that each type has its strengths and limitations, and researchers must carefully consider these when planning their investigations.

Quantitative research, at its core, is about collecting and analyzing numerical data to identify patterns, establish relationships, and test hypotheses. It emphasizes objectivity, measurement, and statistical analysis. The four types we'll discuss are:

1. Descriptive Research:

• Purpose: The primary goal of descriptive research is to describe the characteristics of a population or phenomenon. It aims to answer questions like "what is," "how often," or "how much." It does not manipulate variables or establish cause-and-effect relationships.
• Methods: Common methods include surveys (using questionnaires or interviews with closed-ended questions), observational studies (systematically recording the frequency or characteristics of behaviors or events), and the analysis of existing statistical data (like census data or market research reports).
• Data Analysis: Data is typically analyzed using descriptive statistics such as frequencies, percentages, means, medians, modes, standard deviations, and ranges. Visual aids like tables, bar charts, and pie charts are often used to present the findings.
• Example: A researcher might conduct a survey to determine the average age, income level, and frequency of exercise among residents of a specific city. Another example could be observing and recording the number of times a particular customer service behavior occurs in a retail setting.