PR1

• Sampling - is a small group of people or things taken from a larger group and used to represent the larger group.
• Sampling - is the process of selecting units, like people or organizations, from a population of interest so that by studying the sample we may fairly. generalize our results back to the population from which they were chosen.
• Population - is the entire group that you want to draw conclusions about.
• Sample - is the specific group of individuals that you will collect data from.
• Sample Frame - is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population
• Sample Size - the number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design.
• 2 Primary Types of Sampling Methods:

  1. Probability Sampling
  2. Non-Probability Sampling
• 1. Probability Sampling - involves random selection, allowing you to make strong statistical inferences about the whole group.
• 1. Non-Probability Sampling - involves non-random selection based on convenience or other criteria, allowing you to easily collect data.
• Unit of Analysis - is an object of study within a research project.
• Unit of Analysis - it is the smallest unit a researcher can use to identify and describe a phenomenon— the 'what' or 'who' the researcher wants to study.
• Main Types of Units of Analysis:

  1. Individuals
  2. Groups
  3. Artifacts
  4. Geographical Units
  5. Social Interactions
• 1. Individuals - these are the smallest levels of analysis.
• 1. Groups - these are people who interact with each other.
• 1. Artifacts - these are material objects created by humans that a researcher can study using empirical methods.
• 1. Geographical Units - these are smaller than a nation and range from a province to a neighborhood.
• 1. Social Interactions - these are formal or informal interactions between society members.
• 4 Types of Probability Sampling

  1. Simple Random Sampling
  2. Systematic Sampling
  3. Stratified Sampling
  4. Cluster Sampling
• Simple Random Sampling - every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.
• Simple Random Sampling

  Example: You assign a number to every employee in the company database from 1 to 1000, and use a random number generator to select 100 numbers.
• Systematic Sampling - every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.
• Systematic Sampling

  Example: From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on)
• Stratified Sampling - dividing the population into subpopulations (called strata) that may differ in important ways based on the relevant characteristic.
• Stratified Sampling

  Example: The company has 800 female employees and 200 male employees. Sort the population into two strata based on gender, Men and Women
• Cluster Sampling - dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample then you randomly select entire subgroups.
• Cluster Sampling

  Example: The company has offices in 10 cities across the country, select 3 offices - these are your clusters.
• 5 Examples of Non-Probability Sampling:

  1. Purposive Sampling
  2. Convenience Sampling
  3. Voluntary Sampling
  4. Snowball Sampling
  5. Quota Sampling
• Purposive Sampling - involves the researcher using their expertise to select a sample that is most useful to the purposes of the research
• Purposive Sampling

  Example: You want to know more about the opinions and experiences of disabled students at your university, so you purposefully select a number of students with different disabilities.
• Convenience Sampling - simply includes the individuals who happen to be most accessible to the researcher.
• Convenience Sampling

  Example: You are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a survey. on the topic.
• Voluntary Sampling - is mainly based on ease of access. People volunteer themselves
• Voluntary Sampling

  Example: You send out the survey to all students at your university and a lot of students decide to complete it.
• Snowball Sampling - if the population is hard to access, snowball sampling can be used to recruit participants via other participants.
• Snowball Sampling

  Example: You meet one person who agrees to participate in the research, and he/she puts you in contact with other people that he/she knows in the area.
• Quota Sampling - relies on the nonrandom selection of a predetermined number or proportion of units. This is called a quota.
• Quota Sampling

  Example: You continue recruiting until you reach the quota of 200 participants for each subgroup you have created.
• Data Collection - is a methodological process of gathering and analyzing specific information to proffer solutions to relevant questions and evaluate the results.
• Qualitative Method - is a type of data collection that does not involve numbers or a need to be deduced through a mathematical calculation, rather it is based on the non-quantifiable elements like the feeling or emotion of the researcher.
• Instrument - refers to the devices/instruments used to collect data such as a paper questionnaire or a computer assisted interviewing system.