Research methods

• Null hypothesis


  The hypothesis that there is no significant difference between two groups or variables or that there is no significant effect of an intervention or treatment
• Alternative hypothesis


  The hypothesis that there is a significant difference between two groups or variables or that there is an effect of an intervention/treatment
• Null hypothesis


  Considered the default hypothesis until evidence is provided to reject it
• The null hypothesis is a statement of no effect or no difference
• The alternative hypothesis is the opposite of the null hypothesis
• The alternative hypothesis is the hypothesis that the researcher wants to support
• Hypothesis testing


  1. Collect data

  2. Use statistical tests

  3. Determine if there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis
• Types of variables in experimental research

  • Independent variables
  • Dependent variables
  • Extraneous variables
• If the null hypothesis cannot be rejected, it is assumed to be true until further evidence suggests otherwise
• Independent variable


  The variable that is manipulated or Changed by the researcher in order to observe its effect on the dependent variable. Also known as the cause variable.
• If the null hypothesis is rejected, it is concluded that there is sufficient evidence to support the alternative hypothesis
• Dependent variable


  The variable that is measured or observed in response to changes in the independent variable. Also known as the effect variable.
• Extraneous variables


  Variables that may affect the dependent variable but are not of interest to the researcher. Also known as confounding variables.
• Extraneous variables can include things like age, gender, personality traits, environmental factors, and other variables that may influence the outcomes of the study.
• Researchers try to control for extraneous variables by using random assignment and other research methods to ensure that the groups being compared are as similar as possible.
• Target population


  The group of individuals or entities that a researcher is interested in studying
• It is important to identify and control for extraneous variables to accurately measure the effect of the independent variable on the dependent variable, which helps to ensure that the results of the study are valid and reliable.
• Sample


  A smaller subset of the target population that is selected for the purpose of the study
• Sampling


  The process of selecting a sample from the target population using specific sampling methods
• Main sampling methods


  • Random sampling
  • Opportunity sampling
  • Systematic sampling
  • Stratified sampling
• Random sampling


  • Selecting individuals from the target population at random without any bias or preference
  • Can be done using a random number generator, a table of random numbers, or other random selection methods
• Quantitative methods

  One experimental method
• Strengths of random sampling

  • Considered the most unbiased and representative sampling method
  • Each member of the target population has an equal chance of being selected
  • Minimizes potential for bias and ensures the sample is representative of the target population
  • Easy to understand and implement
• Experimental method


  • Involves the manipulation of an independent variable to observe its effect on a dependent variable
• Weaknesses of random sampling

  • May not always be feasible, especially when the target population is large and spread out
  • Can be time-consuming and expensive to contact every member of the target population
  • May result in a small sample size, which can reduce the generalizability of the findings
• Experimental designs


  • Independent groups
  • Repeated measures
  • Matched pairs
• Opportunity sampling


  • Selecting individuals from the target population based on their availability and willingness to participate in the study
  • Often used in convenient sampling where participants are recruited from a specific location or setting
• Independent groups design

  Suitable for testing the effectiveness of different interventions or treatments
• Strengths of opportunity sampling

  • Easy to implement with minimal resources
  • Can be useful in situations where it is difficult to contact members of the target population
  • May be more practical when time and resources are limited
• Independent groups design

  • Eliminates the problem of order effects
  • Results in fewer demand characteristics
• Weaknesses of opportunity sampling

  • May not be representative of the target population as it only includes individuals who are easily accessible or interested in the study
  • May result in biased samples, which can affect the generalizability of the findings
  • Can be difficult to control extraneous variables that may affect the results
• Independent groups design


  • Individual differences can be confounding
  • May require a larger sample size
• Repeated measures design


  Suitable for testing the effects of one treatment on the same group of participants over time
• Systematic sampling


  • Selecting individuals from the target population at regular intervals, such as every 10th person on a list
• Repeated measures design


  • Eliminates individual differences as a confounding variable
  • Requires fewer participants
• Strengths of systematic sampling

  • Easy to understand and implement
  • Can be useful when the target population is large and spread out
  • Can be more efficient than random sampling as it doesn't require contacting every member of the target population
• Repeated measures design


  • Risk of order effects
  • May lead to demanding characteristics
• Weaknesses of systematic sampling

  • Can introduce an alternative bias if there is a pattern in the list, such as alphabetical order
  • May not be representative of the target population if the list is not complete or up-to-date
  • May result in a small sample size, which can reduce the generalizability of the findings
• Matched pairs design


  Suitable for testing the effects of one treatment on different but similar groups of participants
• Correlation


  A statistical technique used to measure the strength and direction of the relationship between two variables