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Lecture 5.  It is done to ensure the questions asked would generate the data that would answer the research questions n research objectives  The respondents.

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Presentation on theme: "Lecture 5.  It is done to ensure the questions asked would generate the data that would answer the research questions n research objectives  The respondents."— Presentation transcript:

1 Lecture 5

2  It is done to ensure the questions asked would generate the data that would answer the research questions n research objectives  The respondents of pretest or pilot study must be representative of the population  The number of respondents for pretest of survey research is at least 30, because this will allow for minimal size of statistical tests  The way the respondents respond to the questionnaire must be similar as data collection i.e. self-administered or interview

3  Do frequency run for all items in the questionnaire to check for types of data obtained from the respondents  Conduct appropriate statistical tests to answer the research questions n research objectives  Improve the questions according to the responses obtained for better data

4  After we had selected the research problem, developed the research questions and outlined the research objective, then we have to identify how and from where we are going to collect the data to answer the research questions and research objectives  Thus, we need to identify and define the population of our research so that the data collected from them would be the ‘correct’ source of information

5  Population is the entire group being observed  Almost always assumed to be infinite in size, too large and difficult to measure  The total collection of all cases in which the researcher is interested and wishes to understand  Sometime known as universe

6  Population is the group of interest to the researcher, the group to which he/she would like the result of the study to be generalized (Gay & Airasian, 2000)  Population is a group of potential participants to whom you want to generalize the result of the study (Salkind, 2009)

7  Defining the population of study would help to reduce the size and make it more relevant to the study (e.g. working adults, graduate students, career women, Malaysian society etc.)  Study population is the aggregation of elements from which the sample is actually selected (e.g. working adults in private sector, career women in business, redisents in Bandar Baru Nilai etc.)

8  Population are too costly and time consuming to study and the research may not be able to get the data from all of the population…then the only choice is to select sample  Sample – is a subset of the population that is representative of the entire population

9  Sample is subset of population. It comprises some members selected from population.  Elements of population would be there at the sample....... Sample Population

10 Sample size Who will be selected How many n who to choose ? But we must ensure Sample represent population

11  Collecting data from population is called census  Sampling will save cost, time, personnel and yield better data for statistical analysis  Issues in sampling include representativeness, adequate sample size and randomization

12  Representativeness – a sample is representative of the population from which it is selected if the aggregate characteristics of the sample closely approximate those same aggregate characteristics in the population that are relevant to the substantive interest of the study

13  Adequate sample size is necessary for statistical analysis for power and generalization  In getting the sample size, always give allowance for substitutes so that minimum sample size can be obtained  Take note certain statistical procedures require minimum sample size

14  To get the adequate number of sample, one can refer to sample size table that gives the minimum number at predetermined significant level  Another way is to calculate the minimum sample size by using the appropriate formula that takes into account the significant level

15  Randomization – the application of the principles of random sampling to a chunk from a population in which a number of equivalent groups are to be established whose differences from one another can be only attributed to chance

16  Sampling is a process of selecting a number of individual for a study in such way that they represent the larger group from which they were selected (Gay & Airasian, 2000)

17  Probability sampling ◦ The elements in the population have some chance or probability to be selected as a sample (Sekaran, 2003) ◦ Probability sampling is crucial to generalize  Non-Probability sampling ◦ The elements do not have a known or predetermine chance of being selected as a subject. Two major type of Sampling methods

18  Non-probability sampling ◦ Convenience sampling ◦ Purposive sampling ◦ Quota sampling  Probability sampling ◦ Simple random ◦ Systematic random sampling ◦ Stratified random sampling ◦ Cluster or Multi-stage random sampling

19  Probability sampling – sample selected according to mathematical guidelines whereby each unit chance for selection is known  Non-probability sampling does not follow the mathematical probability  Probability allows researcher to calculate the amount of sampling error present in a study, while non-probability sampling does not

20  If sample is chosen according to proper guidelines and is representative of the population, then the results of the sample can be generalized to the population  A sample does not provide the exact data that a population would, the potential error must be taken into account

21  Measurement error occurs when there is inconsistencies produced by testing or evaluation is present  Sampling error is the degree of the measurement of the unit or subject selected differ from those of the population as a whole

22  Sampling error occurs when measurements taken form a sample do not correspond to what exist in the population  Involves confidence level and confidence interval  When conducting research, the researcher estimates the accuracy of the results in terms of a level of confidence that results within a specific interval  Has to be decided before conducting research.

23  In social science the confidence level or significance level is normally 0.05  This means that the researcher is 95 % confident (confidence level) that his/her finding is within + - 5 % (confidence interval) of the true population percentage

24  Four types of probability sampling: - Simple random sampling (SRS): a method of choosing cases form a population by which every case has equal chance of being selected independently. Most commonly used in research. Requires complete population list. E.g. use of random number table, numbers in hat

25 - Systematic sampling: a method of sampling by which the first case from a list of population is randomly selected and then every kth case is selected. Also requires a complete population list. Sampling interval= population size/sample size

26 - Stratified sampling: a method of sampling by which cases are randomly selected from sub-lists of the population. The sampling plan can be either proportional or disproportional. Stratification depends on variables and select sample from each strata randomly. Requires a complete sub-list of the population or stratum

27 - Cluster sampling: a method of sampling by which geographical units are randomly selected and from selected areas, randomly select samples proportionately. Requires a complete list of geographical units

28  Non-probability sampling is done according to certain criteria – the best possible way  The samples must be independent  There should be adequate number of sample  Results cannot be generalized to the population

29  Four issues of using non-probability sampling: * purpose of study – not to be generalize results to population * cost vs. value – cost too high for sample * time constraints – sponsors research that need quick results * amount of accepted error – study where error control is not a prime factor

30  Types of non-probability sampling: - Purposive or judgmental sampling: a method of sampling based on certain judgment or specific purpose - Quota sampling: a method of sampling to include proportionate number of all parties involved - Bias sampling : selected on the basis where researcher favors certain characteristics

31 - Convenient sampling: a method of sampling done as one pleases and feels appropriate - Volunteer sampling: a method of sampling done based on those who likes to participate - Snow ball sampling: a method of sampling done by getting suggestion for next sample from a current sample

32  Purpose of qualitative research * Produce information-rich data * Depth rather than breadth * Insight rather than generalisation  Conceptual rather than numerical considerations * Choose information-rich sites and respondents  Number of respondent is not an issue  Non-probability sampling like snowball technique until saturation

33  Type of research  Purpose of research  Research complexity  Amount of error tolerated  Time constraint  Financial constraint  Previous research in the area

34 Identify the population Determine Sample size Choose Sampling technique

35  The what or whom being studied (Babbie, 2013)  In social science, the most typical units of analysis are the individual people  It is the unit that we want to compare, e.g. organizations, households, families, and interactions  Make sure that the correct source of information (respondent) will generate the right data to be considered as unit of analysis


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