logging in or signing up Population and Sampling * Dr. A. Asgari Azia1980 Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT lite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 1021 Category: Education License: All Rights Reserved Like it (1) Dislike it (0) Added: August 31, 2010 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... By: amitcms (7 month(s) ago) please send this ppt on my mail amitcms@gmaiil.com Saving..... Post Reply Close Saving..... Edit Comment Close By: nicky.dave (8 month(s) ago) can i download it? Saving..... Post Reply Close Saving..... Edit Comment Close By: nicky.dave (8 month(s) ago) can i download it? Saving..... Post Reply Close Saving..... Edit Comment Close By: prarajo (9 month(s) ago) nice presentations useful to professors also Saving..... Post Reply Close Saving..... Edit Comment Close By: shafiullahmateen (12 month(s) ago) please helpe me or give me authority to download this...lolz Saving..... Post Reply Close Saving..... Edit Comment Close loading.... See all Premium member Presentation Transcript POPULATION & SAMPLE : POPULATION & SAMPLE Dr. Azadeh Asgari Research Methodology Population & Sample : Population & Sample POPULATION: all individuals in a group that has similar characteristics (one or more) to be studied by the researcher. e.g.: all counselors; all male teachers teaching in secondary schools; all UPM students Population & Sample : Population & Sample SAMPLE: Part of a chosen population to be observed and analyzed. By observing the randomized samples’ characteristics, several inferences on the population may be made. Differences between sample, subjects, respondents. Parameter & Statistics : Parameter & Statistics Parameter: values obtained from a population. Statistics: values obtained from a sample Randomization : Randomization Basic to scientific observations and research Assumption – even if we cannot precisely predict specific events (e.g.: Individual’s achievement), but we can precisely predict the average/mean achievement of the group Types of Sampling : Types of Sampling Probability Sampling Non-probability Sampling Types of Probability Sampling : Types of Probability Sampling Simple random sampling / selection Systematic sampling Stratified sampling Cluster sampling Randomization of Sample : Randomization of Sample BASIC TO RANDOMISATION = simple randomization = every individual in the group has equal opportunity (equal chance) to be chosen i.e. not biased Choosing one subject is independent of the others . Researcher can assume that the characteristics of the sample approximate the characteristics of total population Sampling Frame : Sampling Frame Assigning a number to all individuals in a population. Using the sampling frame, the sample is chosen / drawn. Simple Random Sampling (selection) : Simple Random Sampling (selection) Using: Fish Bowl Technique Table of Random Numbers Computer Generated Numbers Table of Random Numbers : Table of Random Numbers 1 2 3 4 5 6 7 8 9 10 ______________________________________________________________ 1 10480 15011 01536 02011 81647 91646 69179 14194 62590 36207 2 22368 46573 25595 85393 30995 89198 27982 53402 93965 34095 3 24130 48360 22527 97265 76393 64809 15179 24830 49340 32081 4 42167 93093 06243 61680 07856 16376 39440 53537 71341 57004 5 37570 39975 81837 16656 06121 91782 60468 81305 49684 60672 6 77921 06907 11008 42751 27756 53498 18602 70659 90655 15053 7 99562 72905 56420 69994 98872 31016 71194 18738 44013 48840 8 96301 91977 05463 07972 18876 20922 94595 56869 69014 60045 9 89579 14342 63661 10281 17453 18103 57740 84378 25331 12566 10 85475 36857 53342 53988 53060 59533 38867 62300 01858 17893 Systematic Sampling : Systematic Sampling Steps: Calculate the Interval Draw the Initial Number Select the Other Sample Systematic Sampling : Systematic Sampling In this technique, randomization is done only on the initial number. Drawing the initial number, fixed the other individuals in the sampling frame. Weakness of Systematic Sampling : Weakness of Systematic Sampling There are numbers which do not have equal opportunity to be chosen – thus a slight biasness. Choice of a subject depends on another. Stratified Sampling : Stratified Sampling To reduce sampling error and to increase precision without increasing sample size. To ensure all strata are represented (not different from the population) In a stratum the population is more homogenous e.g.: socio economic status, gender, level of intelligence, level of anxiety If variance is reduced and therefore, sampling error will be reduced Stratified Sampling : Stratified Sampling Steps: Determine the ratio between the strata Ensure the sample size Divide the number of sample according to the initial ratio within the population Select the sample using randomisation technique Cluster Sampling : Cluster Sampling Sampling is according to clusters and not individuals within each cluster Conducted if individuals to be sampled are not known This technique maintained the principles of randomisation Cluster Sampling : Cluster Sampling Need not know individuals within each cluster. If the clusters within the population are far apart . Very suitable and more precise if many small clusters are chosen, therefore similar to the population. Not suitable if a large cluster is chosen since it may not represent the population. Sampling error is even larger if a big and homogeneous cluster is selected. Types of Non-Probability Sampling : Types of Non-Probability Sampling Sample of Convenience or Accidental Sampling Weak sampling procedure Using available cases for the research e.g.: Interviewing the first individual you meet; using you class students; interviewing volunteers Types of Non-Probability Sampling : Types of Non-Probability Sampling Purposive Sampling - Judgment Sampling Sampling element is decided to represent the population. e.g.: Interviewing all possible voters in a district, and using the result to predict the voting pattern for the whole state Sampling Error : Sampling Error Randomized sample may not represent population. Variations my occur, called SAMPLING ERROR . This variation is not an error caused by the researcher, but it occurs as a result of the sampling process. Selection of Biased Sample : Selection of Biased Sample From a telephone directory From a list of magazine subscribers From a list of registered vehicles Sampling Error (e) : Sampling Error (e) Often occurs if the mean sample is used to estimate mean population. Refers to the difference between population parameter and the sample statistics. _ E = x - µ Sample Size : Sample Size Large enough so that it is representative of the population. Crucial issue is representativeness & not the sample size e.g.: Sample of 200 which has been randomly selected is better than a randomly selected sample of 100; but a randomly selected sample of 100 is better than a biased sample of 2.5 million individuals. Aspects in Determining Sample Size : Aspects in Determining Sample Size ECONOMY – researcher’s financial situation MANAGEABLE SAMPEL SIZE by researcher – during data collection VALIDITY – a large enough size needed for high validity RELIABILITY - a large enough size needed for high reliability UTILIZATION OF INFERENTIAL STATISTICS – depends of the type of inferential statistics to be used Descriptive – large Inferential – correlation, minimum 30 Inferential – comparing two groups, 30 for each group Inferential – comparing more two groups, 30 for each group Experimental – small Hypothesis Testing : Hypothesis Testing Testing null hypothesis using different tests based on type of measurement scales and data. Make decision on the null hypothesis. Make decision on the alternative hypothesis. Slide 27: Type I & II Error Scheme HO TRUE HO FALSE REJECT HO ACCEPT HO Type I & II Error : Type I & II Error Type I Error Rejecting a true null hypothesis e.g. Rejecting ho = there exist no relationship between both variables – which is true Type II Error Accepting a false null hypothesis e.g. Accepting ho = there exist no relationship between both variables – which is false Level of Significance : Level of Significance Researcher needs to weigh the consequences of type I and ii errors before conducting the research (how strong the evidence must be before they would reject ho). Level at which ho may be rejected = level of significance Level of Significance : Level of Significance Researcher may avoid type I error by accepting ho all the time. Or avoid type II error by rejecting it all the time. Reducing the value of level of significance (from .05 to .01 or .001) reduces the risk of doing a type I error but increases the risk of doing a type II error. 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Population and Sampling * Dr. A. Asgari Azia1980 Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT lite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 1021 Category: Education License: All Rights Reserved Like it (1) Dislike it (0) Added: August 31, 2010 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... By: amitcms (7 month(s) ago) please send this ppt on my mail amitcms@gmaiil.com Saving..... Post Reply Close Saving..... Edit Comment Close By: nicky.dave (8 month(s) ago) can i download it? Saving..... Post Reply Close Saving..... Edit Comment Close By: nicky.dave (8 month(s) ago) can i download it? Saving..... Post Reply Close Saving..... Edit Comment Close By: prarajo (9 month(s) ago) nice presentations useful to professors also Saving..... Post Reply Close Saving..... Edit Comment Close By: shafiullahmateen (12 month(s) ago) please helpe me or give me authority to download this...lolz Saving..... Post Reply Close Saving..... Edit Comment Close loading.... See all Premium member Presentation Transcript POPULATION & SAMPLE : POPULATION & SAMPLE Dr. Azadeh Asgari Research Methodology Population & Sample : Population & Sample POPULATION: all individuals in a group that has similar characteristics (one or more) to be studied by the researcher. e.g.: all counselors; all male teachers teaching in secondary schools; all UPM students Population & Sample : Population & Sample SAMPLE: Part of a chosen population to be observed and analyzed. By observing the randomized samples’ characteristics, several inferences on the population may be made. Differences between sample, subjects, respondents. Parameter & Statistics : Parameter & Statistics Parameter: values obtained from a population. Statistics: values obtained from a sample Randomization : Randomization Basic to scientific observations and research Assumption – even if we cannot precisely predict specific events (e.g.: Individual’s achievement), but we can precisely predict the average/mean achievement of the group Types of Sampling : Types of Sampling Probability Sampling Non-probability Sampling Types of Probability Sampling : Types of Probability Sampling Simple random sampling / selection Systematic sampling Stratified sampling Cluster sampling Randomization of Sample : Randomization of Sample BASIC TO RANDOMISATION = simple randomization = every individual in the group has equal opportunity (equal chance) to be chosen i.e. not biased Choosing one subject is independent of the others . Researcher can assume that the characteristics of the sample approximate the characteristics of total population Sampling Frame : Sampling Frame Assigning a number to all individuals in a population. Using the sampling frame, the sample is chosen / drawn. Simple Random Sampling (selection) : Simple Random Sampling (selection) Using: Fish Bowl Technique Table of Random Numbers Computer Generated Numbers Table of Random Numbers : Table of Random Numbers 1 2 3 4 5 6 7 8 9 10 ______________________________________________________________ 1 10480 15011 01536 02011 81647 91646 69179 14194 62590 36207 2 22368 46573 25595 85393 30995 89198 27982 53402 93965 34095 3 24130 48360 22527 97265 76393 64809 15179 24830 49340 32081 4 42167 93093 06243 61680 07856 16376 39440 53537 71341 57004 5 37570 39975 81837 16656 06121 91782 60468 81305 49684 60672 6 77921 06907 11008 42751 27756 53498 18602 70659 90655 15053 7 99562 72905 56420 69994 98872 31016 71194 18738 44013 48840 8 96301 91977 05463 07972 18876 20922 94595 56869 69014 60045 9 89579 14342 63661 10281 17453 18103 57740 84378 25331 12566 10 85475 36857 53342 53988 53060 59533 38867 62300 01858 17893 Systematic Sampling : Systematic Sampling Steps: Calculate the Interval Draw the Initial Number Select the Other Sample Systematic Sampling : Systematic Sampling In this technique, randomization is done only on the initial number. Drawing the initial number, fixed the other individuals in the sampling frame. Weakness of Systematic Sampling : Weakness of Systematic Sampling There are numbers which do not have equal opportunity to be chosen – thus a slight biasness. Choice of a subject depends on another. Stratified Sampling : Stratified Sampling To reduce sampling error and to increase precision without increasing sample size. To ensure all strata are represented (not different from the population) In a stratum the population is more homogenous e.g.: socio economic status, gender, level of intelligence, level of anxiety If variance is reduced and therefore, sampling error will be reduced Stratified Sampling : Stratified Sampling Steps: Determine the ratio between the strata Ensure the sample size Divide the number of sample according to the initial ratio within the population Select the sample using randomisation technique Cluster Sampling : Cluster Sampling Sampling is according to clusters and not individuals within each cluster Conducted if individuals to be sampled are not known This technique maintained the principles of randomisation Cluster Sampling : Cluster Sampling Need not know individuals within each cluster. If the clusters within the population are far apart . Very suitable and more precise if many small clusters are chosen, therefore similar to the population. Not suitable if a large cluster is chosen since it may not represent the population. Sampling error is even larger if a big and homogeneous cluster is selected. Types of Non-Probability Sampling : Types of Non-Probability Sampling Sample of Convenience or Accidental Sampling Weak sampling procedure Using available cases for the research e.g.: Interviewing the first individual you meet; using you class students; interviewing volunteers Types of Non-Probability Sampling : Types of Non-Probability Sampling Purposive Sampling - Judgment Sampling Sampling element is decided to represent the population. e.g.: Interviewing all possible voters in a district, and using the result to predict the voting pattern for the whole state Sampling Error : Sampling Error Randomized sample may not represent population. Variations my occur, called SAMPLING ERROR . This variation is not an error caused by the researcher, but it occurs as a result of the sampling process. Selection of Biased Sample : Selection of Biased Sample From a telephone directory From a list of magazine subscribers From a list of registered vehicles Sampling Error (e) : Sampling Error (e) Often occurs if the mean sample is used to estimate mean population. Refers to the difference between population parameter and the sample statistics. _ E = x - µ Sample Size : Sample Size Large enough so that it is representative of the population. Crucial issue is representativeness & not the sample size e.g.: Sample of 200 which has been randomly selected is better than a randomly selected sample of 100; but a randomly selected sample of 100 is better than a biased sample of 2.5 million individuals. Aspects in Determining Sample Size : Aspects in Determining Sample Size ECONOMY – researcher’s financial situation MANAGEABLE SAMPEL SIZE by researcher – during data collection VALIDITY – a large enough size needed for high validity RELIABILITY - a large enough size needed for high reliability UTILIZATION OF INFERENTIAL STATISTICS – depends of the type of inferential statistics to be used Descriptive – large Inferential – correlation, minimum 30 Inferential – comparing two groups, 30 for each group Inferential – comparing more two groups, 30 for each group Experimental – small Hypothesis Testing : Hypothesis Testing Testing null hypothesis using different tests based on type of measurement scales and data. Make decision on the null hypothesis. Make decision on the alternative hypothesis. Slide 27: Type I & II Error Scheme HO TRUE HO FALSE REJECT HO ACCEPT HO Type I & II Error : Type I & II Error Type I Error Rejecting a true null hypothesis e.g. Rejecting ho = there exist no relationship between both variables – which is true Type II Error Accepting a false null hypothesis e.g. Accepting ho = there exist no relationship between both variables – which is false Level of Significance : Level of Significance Researcher needs to weigh the consequences of type I and ii errors before conducting the research (how strong the evidence must be before they would reject ho). Level at which ho may be rejected = level of significance Level of Significance : Level of Significance Researcher may avoid type I error by accepting ho all the time. Or avoid type II error by rejecting it all the time. Reducing the value of level of significance (from .05 to .01 or .001) reduces the risk of doing a type I error but increases the risk of doing a type II error.