# Dissertation questionnaire cover letter

The construction industry is a high-pressure, busy working environment , particularly in a tough economic climate, and completing a student questionnaire may be low on the list of priorities unless there is an existing relationship between the student and the individual. It is important therefore that the questionnaire is as focussed as possible. When deciding on the sorts of question to include, the following considerations should be borne in mind:. The more specific the topic, the easier it will be to compose questions that are suitable.

The list of questions should then be organised as best as possible into one or other of these to give the questionnaire some structure.

See example questionnaire. Open questions have the advantage of allowing the expression of views which can elicit more informative and valuable answers. They are useful when the possible answers may not be known and so it is difficult to pose a closed question with pre-determined options.

However, open questions inevitably demand more of the respondent and may be off-putting if they think it will take more time and effort to answer. Longer, personal answers are also more difficult to analyse compared with closed options that are more easily measurable. These are both easy to construct and quick and easy to answer, so the response rate is likely to be higher.

The drawback is that they can bias the respondent with the available options, and the depth of information obtained is limited. Where scales are used, it can be advisable to select an odd number for the scale. This means the respondent has to make an active decision about whether to choose a higher or lower part of the scale as there is no option to just select the middle option for every answer.

A pilot study is a test run of the questionnaire, giving the opportunity to see how it works in practice in terms of the quality of response. This is a valuable means of assessing whether the wording, structure or layout of the questionnaire is appropriate or could be improved. It might even be advisable to ask test respondents a handful of feedback questions to gauge their thoughts on the questionnaire. Things that might be improved by this feedback include:.

This should be sent out with the invitation to complete the questionnaire.

## Data Collection

It should concisely but accurately explain the purpose of the questionnaire, university course details , and why they would be a suitable respondent this can be very general, i. The better the covering email or letter, the better the chance of a high response rate. The purpose of interviews is to gauge the views and opinions of professionals or others involved in the industry. They should be able to draw from a range of different viewpoints that can then inform the conclusions and recommendations of the dissertation. Open-Ended Questions Survey respondents are asked to answer each question in their own words.

An example would be, "In the last 12 months, what was the total income of all members of your household from all sources before taxes and other deductions? In the previous example, if the question on household income asked respondents to choose from a given set of income ranges instead, it would be considered close-ended. Responses are usually categorized into a smaller list of responses that can be counted for statistical analysis.

Mixed-mode or multi-mode surveys use two or more data collection modes in order to increase survey response. Participants are given the option of choosing the mode that they prefer, rather than this being dictated by the research team. For example, the Head Start Family and Child Experience Survey offers teachers the option of completing the study's teacher survey online or using a paper questionnaire.

Parents can complete the parent survey online or by phone. Therefore, a sample of the members of the population would be selected to represent the total population. A primary strength of sampling is that estimates of a population's characteristics can be obtained by surveying a small proportion of the population. For example, it would not be feasible to interview all parents of preschool-age children in the U. Thus, a sample of preschoolers' parents would be selected and interviewed, and the data they provide would be used to estimate the types of child care parents as a whole choose and their reasons for choosing these programs.

There are two broad types of sampling: Nonprobability sampling : The selection of participants from a population is not determined by chance.

Each member of the population does not have a known or given chance of being selected into the sample. Findings from nonprobability nonrandom samples cannot be generalized to the population of interest. Consequently, it is problematic to make inferences about the population. Common nonprobability sampling techniques include convenience sampling, snowball sampling, quota sampling and purposive sampling.

Probability sampling : The selection of participants from the population is determined by chance and with each individual having a known, non-zero probability of selection. It provides accurate descriptions of the population and therefore good generalizability. In survey research, it is the preferred sampling method. Three forms of probability sampling are described here: Simple Random Sampling This is the most basic form of sampling.

Every member of the population has an equal chance of being selected. This sampling process is similar to a lottery: the entire population of interest could be selected for the survey, but only a few are chosen at random. For example, researchers may use random-digit dialing to perform simple random sampling for telephone surveys.

In this procedure, telephone numbers are generated by a computer at random and called to identify individuals to participate in the survey. Stratified Sampling Stratified sampling is used when researchers want to ensure representation across groups, or strata, in the population. The groups must be mutually exclusive and cover the population. Stratified sampling provides greater precision than a simple random sample of the same size.

Cluster Sampling Cluster sampling is generally used to control costs and when it is geographically impossible to undertake a simple random sample. For example, in a household survey with face-to-face interviews, it is difficult and expensive to survey households across the nation using a simple random sample design.

Instead, researchers will randomly select geographic areas for example, counties , then randomly select households within these areas. This creates a cluster sample, in which respondents are clustered together geographically.

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Survey research studies often use a combination of these probability methods to select their samples. Multistage sampling is a probability sampling technique where sampling is carried out in several stages. It is often used to select samples when a single frame is not available to select members for a study sample. For example, there is no single list of all children enrolled in public school kindergartens across the U.

Therefore, researchers who need a sample of kindergarten children will first select a sample of schools with kindergarten programs from a school frame e. Lists of all kindergarten classrooms in selected schools are developed and a sample of classrooms selected in each of the sampled schools Stage 2. Finally, lists of children in the sampled classrooms are compiled and a sample of children is selected from each of the classroom lists Stage 3. Many of the national surveys of child care and early education e.

Multistage, cluster and stratified sampling require that certain adjustments be made during the statistical analysis. Sampling or analysis weights are often used to account for differences in the probability of selection into the sample as well as for other factors e. Standard errors are calculated using methodologies that are different from those used for a simple random sample.

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## Dissertation Format and Submission: Getting Survey Permissions

The extent to which estimates of the population mean, proportion and other population values differ from the true values of these is affected by these errors. Sampling error is the error that occurs because all members of the population are not sampled and measured. The value of a statistic e. For example, if several different samples of 5, people are drawn at random from the U.

In one sample, Bill Gates may have been selected at random from the population, which would lead to a very high mean income for that sample. Researchers use a statistic called the standard error to measure the extent to which estimated statistics percentages, means, and coefficients vary from what would be found in other samples. The smaller the standard error, the more precise are the estimates from the sample.