Sample design for Blackberry

In sampling, an element is the object (or person) about which or from which the information is desired. In survey research, the element is usually the respondent. A population Is the total of all the elements that share some common set of characterlstlcs. Element: Objects that possess the information the researcher seeks and about which the researcher will make inferences. Population: The aggregate of all elements, sharing some common set of characteristics, that comprise the universe for the purpose of the marketing research roblem.
The researcher can obtain Information about population parameters by taking either a census or a sample. Census: a complete enumaration of the elements of a population or study objects. Sample: A subgroup of the elements of the population selected for participation in the study. sample Large Time available Population size the characteristics Conditions Favoring the use of Factors census Budget Short Large Small small Long Small Variance in Large Cost of sampling error High Cost of nonsampllng errors High Low Nature of measurement Nondestructive Attention to individual cases No
Advantages of Sampling Sampling saves time and money Sampling saves labor. Destructive Yes A sample coverage permits a higher overall level of adequacy than a full enumeration. Complete census Is often unnecessary, wasteful. and the burden on the public. 1) Define the Population: Sampling design begins by specifying the target population, which should be defined in terms of elements, sampling units, extent and time frame. Population/Target population: This is any complete, or the theoretically specified aggregation of study elements. It is usually the ideal population or universe to which esearch results are to be generalized.

Survey population: This is an operational definition of the target population; that is target population with explicit exclusions-for example the population accessible, excluding those outside the country. Element (similar to unit of analysis): This is that unit about which information is collected and that provides the basis of analysis. In survey research, elements are people or certain types of people. Sampling unit: This is that element or set of elements considered for selection in some stage of sampling (same as the elements, in a simple single-stage sample).
In a ulti-stage sample, the sampling unit could be blocks, households, and individuals within the households. Extent: This refers to geographical boundaries. Time frame: The time frame is the time period of interest. In our case; Population/ target population = Blackberry users Survey population = Blackberry users between the age of 18-24, which refers to university students regarding the demographical factors. Elements = Blackberry users who are university students Sampling Unit = Blackberry users in the Business Administration Faculty of Istanbul University. Extent = Business Administration Faculty of Istanbul University
Time Frame = 2 weeks between 4-15 November Given the large size of the target population and limited time and money, it was clearly not TeaslDle to Intervlew tne entlre BlacKDerry users, tnat Is, to take a census. So a sample was taken, and a subgroup of the population was selected for participation in the research. Our sample/ subgroup can be seen above. 2) Determine the Sampling Frame: A sampling frame is a representation of the elements of the target population. To be specific, this is the actual list of sampling units from which the sample, or some stage of the sample, is selected.
It is simply a list of the study population. Sampling frame of our case = List of the students in the Business Administration Faculty of Istanbul University. 3) Select a Sampling Technique: Selecting a sampling technique involves choosing nonprobability or probability sampling. Nonprobability sampling : relies on the personal Judgement of researcher, rather than chance in selecting sample elements. Convenience Sampling: as the name implies, involves obtaining a sample of elements based on the convenience of the researcher. The selection of sampling units is left primarily to the interviewer.
Convenience sampling has the advantages of being both inexpensive and fast. Additionally, the sampling units tend to be accessible, easy to measure, and cooperative. Judgement Sampling: The researcher selects the sample based on Judgement. This is usually and extension of convenience sampling. For example, a researcher may decide to draw the entire sample from one “representative” city, even though the population includes all cities. When using this method, the researcher must be confident that the chosen sample is truly representative of the entire population.
Quota Sampling: introduces two stages to the Judgemental sampling process. The first stage consists of developing control categories, or quotas, of population elements. Using Judgement to identify relevant categories such as age, sex, or race, the researcher estimates the distribution of these characteristics in the target population. Once the quotas have been assigned, the second stage of the sampling process takes place. Elements are selected using a convenience of Judgement process. Considerable freedom exists in selecting the elements to be included in the sample.
The only requirement is that the elements that are selected fit the control characteristics. Snowball sampling: is a special nonprobability method used when the desired sample characteristic is rare. It may be extremely difficult or cost prohibitive to locate respondents in these situations. Snowball sampling relies on referrals from initial subjects to generate additional subjects. While this technique can dramatically lower search costs, it comes at the expense of introducing bias because the technique itself reduces tne II population.
Kellnooa tnat tne sample wlll represent a good ross section Trom tne Probability sampling: in this kind sampling elements are selected by chance, that is, randomly. The probability of selecting each potential sample from a population can be prespecified. Simple 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. Systematic Random Sampling: is often used instead of random sampling.
It is also alled 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. Stratified Random Sampling: is commonly used probability method that is superior to random sampling because it reduces sampling error.
A stratum is a subset of the opulation that share at least one common characteristic. Examples of stratums might be males and females, or managers and non-managers. The researcher first identifies the relevant stratums and their actual representation in the population. Random sampling is then used to select a sufficient number of subjects from each stratum. “Sufficient” refers to a sample size large enough for us to be reasonably confident that the stratum represents the population. Stratified sampling is often used when one or more of the stratums in the population have a low incidence relative to the other stratums.
Cluster Sampling: may be used when it is either impossible or impractical to compile an exhaustive list of the elements that make up the target population. Usually, however, the population elements are already grouped into subpopulations and lists of those subpopulations already exist or can be created. For example, let’s say the target population in a study was church members in the United States. There is no list of all church members in the country. The researcher could, however, create a list of churches in the United States, choose a sample of churches, and then obtain lists f members from those churches. ) Determine the Sample Size: The statistical approaches to determining sample size are based on confidence intervals. These approaches may involve the estimation of the mean or proportion. When estimating the mean, determination of sample size using a confidence interval approach requires a specification of precision level, confidence level, and population standard deviation. In the case of proportion, the precision level, confidence level, and an estimate of the population proportion must De speclTlea. I ne sample size aetermlnea statlstlcally represents ne Tlnal or net sample size that must be achieved.
In order to achieve this final sample size, a much greater number of potential respondents have to be contacted to account for reduction in response due to incidence rates and completion rates. Non-response error arises when some of the potential respondents included in the sample did not respond. The primary causes of low response rates are refusals and not-at-homes. Refusal rates may be reduced by prior notification, motivating the respondents, incentives, proper questionnaire design and administration, and follow- up. The percentage of not-at-homes can be substantially reduced by callbacks.
Adjustments for non-response can be made by subsampling non-respondents, replacement, substitution, subjective estimates, trend analysis, weighting, and imputation. The statistical estimation of sample size is even more complicated in international marketing research, as the population variance may differ from one country to the next. A preliminary estimation of population variance for the purpose of determining the sample size also has ethical ramifications. The Internet and computers can assist n determining the sample size and adjusting it to a count for expected incidence and completion rates.
Sampling distribution: the distribution of the values of a sample statistic computed for each possible sample that could be drawn from the target population under a specified sampling plan. Statistical inference: the process of generalizing the sample results to the population results. Normal distribution: a basis for classical statistical inference that is bell shaped and symmetrical and appearance. Its measures of central tendency are all identical. Standard error: the standard deviation of the sampling distribution of the mean or proportion.
Z values: the number of standard errors in point is away from the mean Incidence rate: the rate of occurrence of persons eligible to participate in a study expressed as a percentage Completion rate: the percentage of qualified respondents to complete the interview. It enables researchers to take into account anticipated refusals by people who qualify Substitution: a procedure that substitutes for nonrespondents other elements from the sampling frame that are expected to respond I rena analysis: a metnoa 0T a0Justlng Tor nonresponaents In wnlcn tne researcner tries to discern a trend between early and late respondents.
This trend is projected to nonrespondents to estimate their characteristic of interest Weighting: statistical procedure that attempts to account for non-response by assigning differential weight to the data depending on the response rate Imputation: a method to adjust for non-response by assigning to characteristic of interest to the nonrespondents based on the similarity of the variables available for both nonrespondents and respondents.