Research

• Objectives: Distinguish population from sample
• Determine sample size by means of Slovins formula

  1. n= sample size

  2. N= total population

  3. e= margin of error

  4. n = 1+ N e^2
• Sampling procedure


  Formal process of choosing the correct subgroup called sample from a population to participate in a research study
• Why is population and sampling important in research?

  • Studies are conducted on samples because it is usually impossible to study the entire population
  • Conclusions drawn from samples are intended to be generalized to the population, and sometimes to the future as well
  • The sample must therefore be representative of the population
• Sample size determination: A sample(n) is a selection of respondents for a research study to represent the total population(N)
• Too many may mean a waste of resources. Too small decreases the utility of the results
• SLOVINS FORMULA in determining the Sample size

  1. n= sample size

  2. N= total population

  3. e= margin of error

  4. n = 1+ N e^2
• EXAMPLE: The researcher wants to conduct a survey. If the population of a big university is 35,000, find the sample if the margin of error is 5%?
• EXAMPLE: Substituting the given data 35,000 n = 1+ (35,000) (.05)2
• EXAMPLE: Substituting the given data 35,000 n = 1+ (35,000) (.0025)
• EXAMPLE: Substituting the given data 35,000 n = 1+ 87.5
• EXAMPLE: Substituting the given data 35,000 n = 88.5 n = 395
• PRACTICE EXERCISE: The researcher wants to conduct a study among 1,500 Grade 12 students enrolled in the STEM Track. Find the sample size if the margin of error is 2%
• PRACTICE EXERCISE: 1,500 n = 1+ (1,500) (.02)2 n = 938
• PROBABILITY SAMPLING PROCEDURES


  • Includes random selection of the samples
  • Each sample or an element has an equal chance of selection under a given sampling technique
• PROBABILITY SAMPLING PROCEDURES

  • Four Types
  • SIMPLE RANDOM SAMPLING: the chance of selection is the same for every member of population
  • SYSTEMATIC RANDOM SAMPLING: follows specific steps and procedures in doing the random selection of the sample
  • STRATIFIED RANDOM SAMPLING: the population is divided into subpopulation called strata. Then, a sample is drawn from each stratum
• PROBABILITY SAMPLING PROCEDURES

  • SIMPLE RANDOM SAMPLING: You want to conduct a survey of 100 JHS students in the school. You printed the names of all students and drew names from box until 100 students were drawn
  • SYSTEMATIC RANDOM SAMPLING: You have a list of 500 persons and you need a sample of 100. you can divide the population size by the sample size by the sample which gives you a value of 5. starting with number 5, take every tenth from the list. This gives you numbers 5, 15,25,35, 45, 55, 65 and so on until you reach sample size of 100
  • STRATIFIED RANDOM SAMPLING: Researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational
• Stratified Random Sampling

  The population is divided into subpopulations called strata. Then, a sample is drawn from each stratum
• Cluster Sampling


  Used when the target respondents in a research study are spread across a geographical location. The population is divided into groups called clusters. A random sampling technique is used on relevant clusters to be included in the study
• Types of Cluster Sampling

  • Single stage cluster sampling: all members from each of the selected clusters are used in the sampling process
  • Two stage cluster sampling: a subset of elements within each selected cluster is selected for inclusion in the sample
  • Multi-stage cluster sampling: more than 2 steps are taken in selecting clusters from clusters
• Cluster Sampling Examples


  • Provinces
  • Towns
  • Cities
  • Barangays
• Non-probability sampling procedures are used when the researcher cannot employ random selection
• Non-probability Sampling Techniques

  • Convenience sampling: a method of selecting samples that are available and capable of participating in a research study on a current issue
  • Snowball sampling: a technique that identifies a key informant about a research topic and then asks the respondents to refer or identify another respondent to participate in the study
  • Purposive sampling: also called judgmental or subjective sampling where samples are chosen for a special purpose
  • Quota Sampling: gathering a representative from a group based on certain characteristics of the population chosen by the researcher
• In Quota Sampling, representatives are gathered based on certain characteristics of the population chosen by the researcher
• Main difference between stratified random sampling and quota sampling

  In quota sampling, non-random selection is used
• Probability Sampling Procedures

  • Simple Random Sampling
  • Systematic Random Sampling
  • Stratified Random Sampling
  • Cluster Sampling
• Non-probability Sampling Procedures

  • Convenience Sampling
  • Snowball Sampling
  • Purposive Sampling
  • Quota Sampling