Research designs​

• research design


  The study's main goals and plan for achieving them should be clearly defined.

  
  

  For instance, a study on social media's impact on teenagers' mental health could involve understanding the link between social media use and mental health through surveys or interviews.
• Experimental


  Experimental research is a way scientists check how one thing causes another. They set up controlled situations, change some things, and see what happens.

  
  

  Imagine you're trying different teaching methods in a classroom.

  
  

  One group of students learns in the usual way, while another tries new methods. By comparing how well they do, you can see which teaching style works best.
• Disadvantages (Experimental)


  Artificiality:

  • Experiments may not apply to real-life situations due to controlled environments.

  • Example: A lab study on social media's effect on mood might not capture everyday feelings.

  
  

  Demand Characteristics:

  • Participants may change their behavior to please the researcher, affecting results.

  • Example: A new pain medicine study might result in participants claiming less pain.
• Advantages (Experimental)

  • Playing with Variables: Scientists alter factors that affect something to observe their effects. For instance, experimenting with different fertilizer types.

  
  

  • Comparing Groups: Having a group without special treatment ensures fair comparison of results. For instance, in a plant experiment, some plants would not receive fertilizer.

  
  

  • Random Selection: Choosing individuals or things randomly to form different groups ensures similarity before tests begin.
• Cross-sectional design 


  Cross-sectional research means gathering information from many people all at once, without changing anything. It's like taking a picture of a moment in time.

  
  

  For example, imagine scientists want to know how common a disease is in a certain group of people right now. They might use cross-sectional research to find out.
• Advantages (Cross-sectional)


  Fairly fast and cheap: Because data is collected all at once, these studies are quicker and cheaper than ones where researchers have to follow people for a long time.

  
  

  Good for first looks: They give a fast picture of what a group of people is like or what's happening at a certain time, which helps to understand a problem a bit before studying it more.
• Disadvantages (Cross-sectional)


  Research that uses data collected only once can't prove for sure what causes what, like how much teenagers use social media affecting how they feel about themselves.

  
  

  What people experienced before or what's happening now might affect how they respond, which can make the data unreliable.
• Longitudinal design


  Longitudinal research is when scientists study the same group of people for a long time, checking the same things over and over again. This helps them see how things change and affect each other over time.
• Advantages (Longitudinal)


  Cause-and-Effect Relationships: Longitudinal studies, unlike pictures taken at one time, can show why something happens. Researchers watch people for a while to see how one thing, like exercising more, makes another thing, like weight, change later.

  
  

  Understanding Change: Longitudinal studies are great for seeing how people and things change over time. They help us see how people grow, how diseases get worse, and what happens after we try to help.
• Disadvantages (Longitudinal)


  Time and Cost: Long-term studies can take a lot of time and money. They need both researchers and participants to stick around for a while, but some might leave over time.

  
  

  Maturity and History Effects: People might act differently just because they know they're being studied (maturity effect). Also, things that happened before might affect how they act later (history effect).