1 / 209

SAMPLING AND DATA COLLECTION

SAMPLING AND DATA COLLECTION. SAMPLING AND DATA COLLECTION. Prepared by : Dr. Priya Reshma Aranha Asst. Professor Dept of Child Health Nursing Yenepoya Nursing College. Reviewed by: Mr. Girish GR Ms. Vinitha Dsouza. Learning Objectives.

judithortiz
Download Presentation

SAMPLING AND DATA COLLECTION

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. SAMPLING AND DATA COLLECTION Priya Reshma Aranha

  2. SAMPLING AND DATA COLLECTION Prepared by : Dr. PriyaReshmaAranha Asst. Professor Dept of Child Health Nursing Yenepoya Nursing College Reviewed by: Mr. Girish GR Ms. VinithaDsouza Priya Reshma Aranha

  3. LearningObjectives At the end of the class the students will be able to, • Define sample and sampling • Explain various sampling techniques • Discuss various methods used for data collection • Measure the reliability of data collection tool • Appreciate validity of data collection tool • Explain pilot study • Enumerate the procedure of data collection Priya Reshma Aranha

  4. Evaluation • Define research • What is the importance of informed consent in research? • List down the characteristics of good research.. • What are the steps of research process? Priya Reshma Aranha

  5. References • Denise F Polit & Cheryl Tetano Beck. Essentials of Nursing Research: Appraising Evidence for Nursing Practice. Walter Kluwer, Lippincott Williams & Wilkins. 7th edition • Suresh K Sharma. Nursing research & Statistics., Elsevier.2011 • Denise F Polit, Cheryl Tatano Beck. Principles and methods. Lippincott Williams & Wilkins. 7th edition • Sukhpal Kaur, Amarjeet Singh. Simplified nursing research and statistics for undergraduates. CBS. 1st edition

  6. THANK YOU Priya Reshma Aranha

  7. SAMPLING AND DATA COLLECTION

  8. SAMPLING

  9. Terminologies • Population : Aggregation of all the units in which a researcher is interested OR The set of people to which the investigator wants to generalize his results • Target population : Total number of people or objects which are meeting the designated set of criteria. • Accessible population : Aggregate of cases that conform to designated criteria , are also accessible as study subjects

  10. Sampling : Process of selecting a representative segment of the population under study • Sample : Representative unit of a target population • Element / Sampling Unit : Individual entities / smallest unit from which sample can be selected • Sampling frame : List of all the elements or subjects in the population from which the sample is drawn

  11. Sampling error : Fluctuations in the values of the statistics of characteristics from one sample to another • Sampling bias : Distortion that arises when a sample is not representative of the population • Sampling plan : method, size, procedure etc

  12. How to select sample

  13. Sampling breakdown

  14. Purposes • Economical • Improved quality of data • Quick study results • Precision and accuracy of data

  15. Characteristics of good sample • Representative • Free from bias and errors • No substitution and incompleteness • Appropriate sample size

  16. Sampling process • Identify and define target population • Describe the accessible population and ensure sampling frame • Specify the sampling unit • Specify sample selection methods • Determine sample size • Specify sampling plan • Select the desired sample

  17. Factors influencing sampling process

  18. Types of sampling technique Probability sampling Non probability sampling Purposive sampling Convenience sampling Consecutive sampling Quota sampling Snow ball sampling • Simple random sampling • Stratified random sampling • Systematic random sampling • Cluster / multistage sampling • Sequential sampling

  19. Types of Sampling Probability sample – a method of sampling that uses of • random selection so that all units/ cases in the population • have an equal probability of being chosen • Absence of bias Non-probability sample – does not involve random • selection and methods are not based on the rationale of probability theory.

  20. SIMPLE RANDOM SAMPLING • Applicable when population is small, homogeneous & readily available • All subsets of the frame are given an equal probability. • Each element of the frame thus has an equal probability of selection. Types : • A table of random number • Lottery system • Use of computer

  21. Simple random sampling

  22. Table of random numbers 6 8 4 2 5 7 9 5 4 1 2 5 6 3 2 1 4 0 5 8 2 0 3 2 1 5 4 7 8 5 9 6 2 0 2 4 3 6 2 3 3 3 2 5 4 7 8 9 1 2 0 3 2 5 9 8 5 2 6 3 0 1 7 4 2 4 5 0 3 6 8 6

  23. Lottery system

  24. Advantages : • Easy way, fair way • Requires minimum knowledge about population • Most unbiased • Free from sampling errors • Sample errors can be easily computed and the accuracy of estimate easily assessed. Disadvantages : • Complete up to date information regarding population needed

  25. Stratified Random Sampling

  26. Stratified Random Sampling • Used for heterogeneous population . • Researcher divide the entire population into subgroups or strata and randomly select subjects proportionally from strata . • Strata – Age, Gender , Religion , Socio-economic status ,Diagnosis , Education ,Geographic region ,Type of institution ,Type of care ,Type of registered nurses ,Nursing area specialization ,site of care, etc.

  27. Types • Proportionate stratified sampling : Each stratum is in proportion to the size of the population . • Disproportionate stratified sampling: In this subtype, the sample chosen from each stratum are not in proportion to the size of the total population in that stratum

  28. Example for disproportionate stratified sampling Example for Proportionate stratified sampling

  29. Merits • It ensures representation of all groups in a population. • Comparison is possible between subgroups with this sampling technique. • The researcher can representatively sample even the smallest and most inaccessible subgroups in the population. • There is a higher statistical precision. • It saves a lot of time, money and effort to the researcher .

  30. Demerits • Requires accurate information of the proportion of population in each stratum. • Large population must be available from which to select subjects. • Possibility of faulty classification and hence increases in variability.

  31. Systematic random sampling • Systematic random sampling can be linked to an arithmetic progression between any two consecutive numbers is same. • It involves in the selection of every K th case from list of groups. • K=N/m or K= Number of subjects in target population Size of the sample

  32. Merits • Convenient & simple to carry out • Even spread of sample • Less cumbersome, time consuming, cheap • More efficient and better representative

  33. Demerits • If not randomly selected, it becomes non randomized sampling • Sometimes it may be biased • If sampling frame has non randomly distributed subjects, this sampling technique may not be appropriate

  34. Cluster or multistage sampling • Done when simple random sampling is impossible because of size • Here random selection of sampling unit • Then from each unit sample is drawn in simple or stratified sampling technique • Geographical units are commonly used

  35. Cluster sampling Section 1 Section 2 Section 3 Section 5 Section 4

  36. Types • One stage cluster sampling : One stage cluster • Two stage cluster sampling : Two stage cluster

  37. Two types of cluster sampling methods One-stage sampling. All of the elements within selected clusters are included in the sample. Two-stage sampling. A subset of elements within selected clusters are randomly selected for inclusion in the sample.

  38. Multi-stage sampling • Complex form of cluster sampling in which two or more levels of units are embedded one in the other. • First stage, randomnumber of districts chosen in all states • Second stage: random number of talukas, villages. • Third stage : units will be houses. • All ultimate units (houses, for instance) selected at last step are surveyed.

  39. Merits • Cheap, quick and easy • Large population can be studies • District ….villages….. • Some cluster may be again used for the study

  40. Demerits • Least representative • High sampling error • In case of small homogenous population, this technique will not work

  41. Sequential sampling • Here the sample size is not fixed • The investigator initially selects small sample and tries to make inferences . If not able to draw results, he or she then adds more subjects until clear cut inference are drawn

  42. Sequential sampling

  43. Merits • Best for small representative sample • Help in ultimately finding the inferences Demerits • Phenomena to be studied at one point of time is not possible • Requires repeated entries into the field to collect the sample

  44. Non probability sampling • Every subject does not have an equal chance to be selected as sample • A non random selection, generalization is not possible

  45. Features • Does not give all individuals an equal chance to be a part of the study • Researches – time bound, money, workforce (limitations for probability) , non probability sampling techniques used • Sample selected on the basis of accessibility or by purposive personal judgment of the researcher • Sample may or may not represent the entire population

  46. uses • When a particular trait exists in the population • Used in qualitative, pilot or exploratory studies • Used when randomization is not possible • Done when the results generated will not be used to create generalization • When the researcher has limited budget, time and workforce • Can be used in initial studies like pilot studies

  47. types • Purposive sampling • Convenience sampling • Consecutive sampling • Quota sampling • Snowball sampling

  48. Purposive sampling • Judgmental or authoritative sampling • Subjects chosen with a specific purpose in mind • Require in depth knowledge about the accessible population • Used when a limited number of individuals possesses the trait of interest

More Related