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Where do you get what you are telling us?

Where do you get what you are telling us?. Sources of Data where do you get the data? And from whom should the data be collected? Clearly, your data should come from the participants that are both available to you and relevant to the question you are studying.

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Where do you get what you are telling us?

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  1. Where do you get what you are telling us?

  2. Sources of Data where do you get the data? And from whom should the data be collected? Clearly, your data should come from the participants that are both available to you and relevant to the question you are studying

  3. there are times when we aren't very concerned about generalizing. we're just evaluating a program in a local agency and we don't care whether the program would work with other people in other places and at other times. In that case, sampling and generalizing might not be of interest

  4. "Who do you want to generalize to?" Or should it be: "To whom do you want to generalize?" In most applied social research, we are interested in generalizing to specific groups. The group you wish to generalize to is often called the population This is the group you would like to sample from because this is the group you are interested in generalizing to

  5. Some examples of population • All secondary school principals in Malaysia • All primary school counselors in the state of Sabah • All students attending Kolej Tunku Kursiah during the academic year 2004-2005 • All students in Mrs. Amin form two at SMKA

  6. Let's imagine that you wish to generalize to urban homeless males between the ages of 30 and 50 in Malaysia. If that is the population of interest, you are likely to have a very hard time developing a reasonable sampling plan. You are probably not going to find an accurate listing of this population, and even if you did, you would almost certainly not be able to mount a national sample across hundreds of urban areas.

  7. So we probably should make a distinction between the population you would like to generalize to, [theoretical population or target population ] The population that will be accessible to you [accessible population]. In this example, the accessible population might be homeless males between the ages of 30 and 50 in six selected urban areas in Malaysia

  8. A population can be defined as any set of persons/subjects having a common observable characteristic. It is the group from which you were able to randomly sample . The target population is the group to which the researcher would like to generalize his or her results. This defined population has at least one characteristic that differentiates from other groups. The accessible population is the population to which the researcher has access.

  9. Once you've identified the theoretical and accessible populations, you have to do one more thing before you can actually draw a sample -- you have to get a list of the members of the accessible population. The listing of the accessible population from which you'll draw your sample is called the sampling frame. If you were doing a phone survey and selecting names from the telephone book, the book would be your sampling frame. That wouldn't be a great way to sample because significant subportions of the population either don't have a phone or have moved in or out of the area since the last book was printed.

  10. Finally, you actually draw your sample (using one of the many sampling procedures). The sample is the group of people who you select to be in your study. Sampling refers to drawing a sample (a subset) from a population (the full set). is the act, process, or technique of selecting a suitable sample, or a representative part of a population for the purpose of determining parameters or characteristics of the whole population. Samples are measured in order to make generalisations about populations. Ideally, samples are selected, usually by some random process, so that they represent the population of interest.

  11. Topic if investigatiion: The effect of computer assisted instruction on The reading achievement of first and second graders in Malaysia

  12. The usual goal in sampling is to produce a representative sample (i.e., a sample that is similar to the population on all characteristics, except that it includes fewer people because it is a sample rather than the complete population). In other words, a representative sample is a "mirror image" of the population from which it was selected.

  13. Why Sample? • First, it is usually too costly to test the entire population • The second reason to sample is that it may be impossible to test the entire population • The third reason to sample is that testing the entire population often produces error. Thus, sampling may be more accurate.

  14. Why Sample? • The final reason to sample is that testing may be destructive. • [you probably would not want to buy a car that had the door slammed five hundred or a thousand times or had been crash tested. Rather, you probably would want to purchase the car that did not make it into either of those samples]

  15. Up to here by six 27/08/05

  16. How important is sampling? Sampling is important in regards to external validity. • What is external validity? • The extent to which the result of the study can be generalized. • Two types : population and ecological generalizibility

  17. Next lecture begins here

  18. Population generalizability: • The degree to which the sample represent the population • Look at the usefulness of the study > small and narrowly defined groups: findings not useful • That is why representativeness is important. We want to make the result of the study to be widely applicable as possible. • You must take appropriate action to make sure the findings are generalized to the entire population.

  19. Ecological generalizability • Refers to the degree the result of the study can be extended to other settings. • Example: result from urban school may not be true for students from rural schools • What we can do here is to describe in detail the nature of the environment, setting under which the study takes place.

  20. You can’t generalized the effectiveness of a method of teaching mathematics to the effectiveness of the methods for all subjects. Caution: even with the application of powerful technique of random sampling, it is quite difficult to overcome the problem of ecological gerenalizibility.

  21. Procedure for Drawing a Sample 1. Define the population. Who is the population for each project? –e.g., residents of bandar Kajang or around Bandar Kajang. Remember, the population is the group you want to infer to from the sample - define it carefully so it is clear who is in, and who is out. 2. Identify the sampling frame: the list of elements from which the sample may be drawn. –It is sometimes referred to as the working population. –e.g., to sample teachers, my sampling frame might be a list from the The Education Department of Hulu Langat District 3. Select a sampling procedure

  22. DEfine the population and sample clearly, why? For those interested to determine the generalizibility of the findings Not only define the population and sample, sampling process has to be clearly defined too. (this one of the common weaknesses in research)

  23. In non-experimental research, you investigate relationships among variables in some pre-defined population. Typically, you take elaborate precautions to ensure that you have achieved a representative sample of that population; You define your population, then do your best to randomly sample from it.

  24. The two main types of sampling in quantitative research: random sampling [probability ] nonrandom sampling. [nonprobability ]  The former produces representative samples.  The latter does not produce representative samples.

  25. In probability samples, each member of the population has a known probability of being selected. Elements are drawn by chance procedures Probability methods include random sampling, systematic sampling, and stratified sampling. In nonprobability sampling, members are selected from the population in some nonrandom manner. These include convenience sampling, judgment sampling, quota sampling, and snowball sampling.

  26. Probability-based (random) samples: These samples are based on probability theory. Every unit of the population of interest must be identified, and all units must have a known, non-zero chance of being selected into the sample. Every member of the population has an equal chance of being selected (those selected and those who are not are similar to one other). The idea here is representativeness. How sure are we? That is why it has to be random and sufficiently large!!! should have no bias. The researcher cannot consciuosly or unconsciously influence who will be selected

  27. The advantage of probability sampling is that sampling error can be calculated. Sampling error is the degree to which a sample might differ from the population. It the difference between population parameter and sample statistics (you can’t run away from sampling error unless you do census)

  28. When inferring to the population, results are reported plus or minus the sampling error. In nonprobability sampling, the degree to which the sample differs from the population remains unknown.

  29. Random sampling is the purest form of probability sampling. Each member of the population has an equal and known chance of being selected. When there are very large populations, it is often difficult or impossible to identify every member of the population, so the pool of available subjects becomes biased RANDOM = each element of the population has an equal chance of inclusion in the sample. Begin with a SAMPLING FRAME = a list of every element in the population.

  30. Random Sampling Techniques • Simple random sampling • The first type of random sampling is called simple random sampling. • It's the most basic type of random sampling. •  It is an equal probability sampling method (EPSM). •   EPSEM means "everyone in the sampling frame has an e qual chance of being in the final sample." • EPSEM is important because that is what produces "representative" samples (i.e., samples that represent the populations from which they were selected)!

  31. Simple random sample: Each unit in the population is identified, and each unit has an equal chance of being in the sample. The selection of each unit is independent of the selection of every other unit. Selection of one unit does not affect the chances of any other unit.

  32. A A E SIMPLE RANDOM

  33. Sampling experts recommend random sampling "without replacement" rather than random sampling "with replacement" because the former is a little more efficient in producing representative samples (i.e., it requires slightly fewer people and is therefore a little cheaper).

  34. Advantages of the SRS method of sampling: • Assures good representativeness of sample (particularly if large). • allows us to make generalizations/inferences. In fact, most of the statistical stuff we'll do later assumes that we've actually done a simple random sample, even if we haven't. • avoids biases that are possible in some of the other methods we'll talk about.

  35. Disadvantages of SRS method: • Have to have a list/sampling frame. • Have to number the list. • both are hard to do when the population is large.

  36. How do you draw a simple random sample?" • One way is to put all the names from your population into a hat and then select a subset (e.g., pull out 100 names from the hat).  • Researchers typically use a computer program that randomly selects their samples. One program is available at the following address: http://www.randomizer.org/form.htm . • Can use excel to generate random numbers. You need as many randomly generated numbers as elements in your sample (n).

  37. To use a computer program (sometimes called a random number generator) you must make sure that you give each of the people in your population a number. Then the program gives you a list of randomly selected numbers. Then you identify the people with those randomly selected numbers and try to get them to participate in your research study

  38. Researchers often use a table of random numbers. You pick a place to start, and then move in one direction (e.g., move down the columns). Use the number of digits in the table that is appropriate for your population size (e.g., if there are 2500 people in the population then use 4 digits). Once you get the set of randomly selected numbers, find out who those people are and try to get them to participate in your research study.

  39. For example, to select a sample of 25 people who live in your college dorm, make a list of all the 250 people who live in the dorm. Assign each person a unique number, between 1 and 250. T Then refer to a table of random numbers. Starting at any point in the table, read across or down and note every number that falls between 1 and 250. Use the numbers you have found to pull the names from the list that correspond to the 25 numbers you found. These 25 people are your sample. This is called the table of random numbers method.

  40. Kita perlu ada frem sampel yang lengkap bagi membolehkan kaedah ini diamalkan. Jika tak ada frem yang lengkap, apa nak buat? Kelemahan procedure ini perlukan sampling frem yang lengkap. Best sampling procedure !!! dengan andaian tertentu. The key to obtaining random sampel is to ensure that every member of the population has an equal and independence chance of being selected. So kita gunakan table of random numbers (more scientific)

  41. How to use table of random numbers? • Say you have 300 sampel to be selected out of 3000 students. • Start anywhere on the table you have chosen (possibly secara random) • Mulakan membaca nombor 4 digit (why 4 digits > sebabnya the final number 3000 adalah empat digit] • Pilih nombor yang tidak melebihi 3000 sehinggalah bilangan sampel yang diperlukan mencukupi • What if you come across two similar number? Skip the later number and go to the nest number. • When selecting the number you can either go horizontally or downwards. • Do not use simpel random sampling if we wish certain subgroups to be in the sample.

  42. Boleh tak kita memilih secara persampelan rawak mudah guru-guru di Malaysia?- rasionale? Kalau tak mampu nak buat simple random sampling , do cluster random, stratified random or multi-stage random. Kalau nak pastikan certain sub-group yang sememangnya mempunyai banyak berbezaan ciri to be included, use stratified random sampling

  43. Systematic sampling Is often used instead of random sampling. It is also called an Nth name selection technique. After the required sample size has been calculated, every Nth record is selected from a list of population members. As long as the list does not contain any hidden order, this sampling method is as good as the random sampling method. Its only advantage over the random sampling technique is simplicity. Systematic sampling is frequently used to select a specified number of records from a computer file.

  44. Advantages of Systematic Sampling : Easier to do than SRS. You don't have to keep running back to the random number generator. Disadvantages of Systematic Sampling: Still need a list/sampling frame that is numbered. Might run into periodicity problem. If the list happened to be arranged by class (1,2,3,4…), you might end up picking all first years. Have to make sure the list is not so structured.

  45. Systematic sampling • Systematic sampling involves three steps: • First, determine the sampling interval, which is symbolized by "k," (it is the population size divided by the desired sample size). • Second, randomly select a number between 1 and k, and include that person in your sample. • Third, also include each kth element in your sample. For example if k is 10 and your randomly selected number between 1 and 10 was 5, then you will select persons 5, 15, 25, 35, 45, etc. When you get to the end of your sampling frame you will have all the people to be included in your sample.

  46. For example, To select a sample of 25 dorm rooms in your college dorm, (1) Make a list of all the room numbers in the dorm. Say there are 100 rooms. (2) Divide the total number of rooms (100) by the number of rooms you want in the sample (25). The answer is 4. This means that you are going to select every fourth dorm room from the list. But you must first consult a table of random numbers. (3) Pick any point on the table, and read across or down until you come to a number between 1 and 4. This is your random starting point. Say your random starting point is "3". This means you select dorm room 3 as your first room, and then every fourth room down the list (3, 7, 11, 15, 19, etc.) until you have 25 rooms selected.

  47. Systematic Sample/Skip Interval Sample 1. Begin with a numbered sampling frame again. 2. Choose your random number. 3. Choose your SAMPLING INTERVAL = number in population divided by number desired in sample, or N/n. 4. Select the element that corresponds to the random number. Then instead of picking a second random number, etc., count out the interval (N/n) and choose that element. When you get to the end of the list go back to the beginning until you have your full sample. Note, if you get a fraction, round up. If you round down, you might not get to the end of the list, and those elements at the end will not have any probability of inclusion. With rounding up, you will always get through the whole list.

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