PR2 Sampling

• It refers to an individual that represents the entire population of target respondents of the study.

  Sample
• This is the subgroup of the population.

  Sample
• It refers to a group of individuals that the researcher is interested in

  studying and usually has common or similar characteristics.

  Population
• It refers to the number of elements in the population that included

  in the study.

  Sample size
• It refers to a complete list of all cased in the population from

  which the sample will be drawn (e.g. master list of Grade 12 students in a certain

  school).

  Sampling Frame
• One of the most common statistical formulas used by researchers in determining sample size is Slovin’s formula.
• Slovin’s formula is a statistical formula used to obtain an accurate sample size (n) given the population (N) and margin of error (e).
• Slovin’s formula is a statistical formula used to obtain an accurate sample size (n) given the population (N) and margin of error (e).
• The margin of error (e) is the allowable error margin in research.
• Slovin's Formula calculates the number of samples

  required when the population is too large to directly sample every member.
• The selection of components of the sample that will give a representative view of the whole is known as the sampling technique
• Selecting samples can be biased or unbiased.
• Probability Sampling refers to a sampling technique in which samples are obtained using some objective chance mechanism, thus involving randomization.
• Probability sampling techniques give all elements of the population an equal chance of being selected but using this technique may consume a lot of time and effort of the researchers.
• if your population is LESS THAN 50, go away from probability sampling
• your sample size should be AT LEAST 30.
• Types of Probability Sampling Techniques

  Simple Random Sampling, Systematic Random Sampling, Stratified Random Sampling, Cluster Sampling
• This is the basic probability sampling design in which

  the chance of selection is the same for every member of the population.

  Simple Random Sampling
• To conduct this sampling technique, the researcher should ensure first that he/she has the complete list of all the elements (sampling frame) of his/her target population.

  Simple Random Sampling
• the sample is drawn so that all elements have equal number of chances to be selected.

  Simple Random Sampling
• ways of selecting simple random sampling

  By utilizing a TABLE OF RANDOM NUMBERS, By using the LOTTERY TECHNIQUES/FISHBOWL METHOD, By using DIGITAL RANDOM PICKER APPLICATION
• the researcher puts the 800 names of Grade 12 students in a box

  and then pick only 470 names to participate in his study. What type of sampling is this?

  Simple Random Sampling
• It is a sampling that follows regular intervals from a

  list.

  Systematic Random Sampling
• It has specific steps and procedures in doing the random selection of the samples.

  Systematic Random Sampling
• With this sampling technique, it may spread the selected samples evenly across the entire population than simple random sampling.

  Systematic Random Sampling
• The population is divided into groups called strata

  and then simple random sampling is applied in selecting samples from each group.

  Stratified Random Sampling
• This is the best random sampling method when the researcher wishes to have a representative sample of population.

  Stratified Random Sampling
• The largest scale surveys

  used the cluster sampling method
• It is used when

  the target respondents in a research study are

  spread across a GEOGRAPHICAL LOCATION.

  Cluster Sampling
• In this method, the population is group into what we called CLUSTER. Simple random sampling is used in selecting the cluster.

  Cluster Sampling
• non-probability sampling is a sampling technique that does not give all the samples in the population equal chances of being selected.
• The selection of samples is

  based on the subjective judgment or biased decision of the researchers (Faltado et al

  2017).

  Non-Probability Sampling
• Using non-probability sampling could be less time-consuming and less hassle for the researchers.
• The downfall of this sampling method is that an unknown part of the

  entire population is not sampled.

  Non-probability Sampling
• This means that the sample may or may not accurately

  represent the entire population. Thus, the results of the research study might not be used in generalizations referring to the entire population (explorable.com 2009).

  Non-Probability Sampling
• Types of Non-Probability Sampling

  Purposive Sampling, Quota Sampling
• This is also called judgmental or subjective sampling. In

  this method, the researcher chooses only those respondents that he thinks are suitable to participate in his research study.

  Purposive Sampling
• The researchers conducted a study on why Grade 11 chooses TVL tracks over Academic tracks. They found samples by asking first the question “Are you planning to go to the university?”. Those who would say “Yes” would not be included in the study because the aim of the researchers was to have respondents who would work after graduating from senior high school. What type of Non-probability Sampling is this?

  Purposive Sampling
• It is a sampling technique wherein the researcher makes sure

  of equal or proportionate representation of subjects depending on which trait is considered as a basis of the quota.

  Quota Sampling
• The bases of the quota are usually age, gender, education, race, religion, and socioeconomic status.