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Make sure each table you use has a heading and is clearly labelled.The table should be fully understandable on its own. Tables are normally numbered.

nominal, ordinal interval/ratio or dichotomous.

Consider the nature of the variables being analysed e.g.

Does your analysis provide an answer to your research question?.

Do not confuse statistical significance with substantive significance.

SPSS or R) and how you can manipulate and present your data in these systems.

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Become familiar with computer software (e.g.Choose your sample carefully to avoid sampling error or sampling variability.Carefully and clearly define your variable of interest.Carefully and clearly define your population.This will have implications for the type of analysis you can do later. You need to think about the kind of data you will be collecting and the sample size.Always consider how you will analyse your data when you are designing your research instrument, NOT after you have collected your data.Purposive sampling - sampling with a specific purpose in mind e.g females between 50 and 60. Quota sampling - Participants are selected in a non-random way according to a fixed quota e.g using a quota such as 40% female and 60% male You start with one in your sample who then recommend others. Snowballing - a variant of convenience sampling. sex or age.Ĭluster sampling - divide the population into clusters and randomly sample a number from these clusters.Ĭonvenience sample - this is a sample that is conveniently available to you. The number from each group should be proportionate to the number in the general population e.g. Stratified sampling - use if you are aware that specific groups in a population will be different from each other. choosing every Xth member of the population. Systematic sampling - a random sample where a system has been applied e.g. Random sample - every member of a population has an equal chance of being selected e.g drawing lots out of a hat. Sampling variability concerns the situation where if you took repeated samples from the same population and then work out the means and standard deviations of these different samples they will not be the same. The higher the error the less certain you can be that the value reflects what you would expect in the population. This error will be less if we thought about the sample carefully, ensuring representativeness and avoiding any biases. A n estimate of the Mean from the sample will not be be the same as the population value. how does the sample you are looking at reflect / is different from the population you want to expand your hypothesis to.Į.g. Sampling error is an estimate of how a sample statistic is expected to differ from a population parameter in a random sample drawn from the population i.e. Sampling is often based on randomness to ensure representativeness, although non-random sampling might be used in some circumstances. Although in social research these units are often individuals, they do not need to be but could be households, firms, countries etc. You`ll be the first to hear about new designs, trending news, tips and tricks, sales, coupons and so on.Sampling is the process of selecting a given number of units of analysis from a population.

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