Sampling Procedure and Sample - Research Reviewer PDF

Population Time and Cost - totality of all the objects, elements, - choosing samples makes you deal persons, and characteristics under with one big whole population, with consideration each member of th...

Population Time and Cost - totality of all the objects, elements, - choosing samples makes you deal persons, and characteristics under with one big whole population, with consideration each member of this large group - possesses common characteristics needing your attention, time and about which the research aims to effort explore - Approaches in Identifying the Sample Types of Population: Size 1. Target population 2. Accessible population Research Design Number of Subjects & Participants Sampling - systematic process of selecting the Descriptive 10% to 20% maybe group to be analyzed in the research Research required study Comparative 15 students or Research groups Sample Size - reflecting 95% distribution of the population or of a sample Literature Review representing the whole population is - reading similar or related literature highly Probable and studies to your current research study Sampling Technique - fall under two categories: Formula - established for the computation of A. Probability Sampling an acceptable sample size. The - uses a random selection common formula is Slovin’s Formula B. Non-probability sampling Power of Analysis - a purposive or controlled selection - founded on the principle of power analysis Heterogeneity of Population - two principles you need to consider - composed of individuals with varied if you are going to use this abilities approach: these are statistical power - wide variation among the people and effect size composing the Population Statistical Power Statistical Techniques - probability of rejecting the null - The accuracy of the sample hypothesis depends also on how precise or - suggests that indeed there is a accurate your methods are relationship between the independent and dependent variables of the research study  Stratified Sampling Effect Size - group the respondents according to - Level of difference between the a particular characteristic so that experimental group and the control each group is “homogenous” group Cluster Sampling - group the respondents according to, for example, geographic closeness (a “cluster”) Non-Probability Sampling - the researcher can select a specific - Not chosen randomly but number of schools and test all the purposefully students in those selected schools - Not randomized, they are susceptible to bias Cons of Cluster Sampling - may build in bias Quota Sampling - answers accessibility of samples Voluntary Sampling - fails in representativeness of Purposive Sampling samples Purposive Sampling - mostly in small-scale research Snowball Sampling - researcher must comment on generalizability Probability Sampling - more clusters, the better - makes you base your selection of - sample lightly per cluster respondents on pure chance - In this case, everybody in the Stage Sampling population participates - involves selecting the sample in stages, that is, taking samples from Simple Random Sampling samples Systematic Sampling - the purpose of sampling is Stratified Sampling consistent in each stage until the Cluster Sampling desired sample size is achieved Simple Random Sampling (SRS) - useful when the entire population list is available - random sampling with replacement - chosen element is placed back to the “fishbowl” - an element can be chosen twice - random sampling without replacement