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Generally on the surface you can use data analyses like normality test (deciding to use parametric / non-parametric statistics), descriptive statistics, reliability test (Cranach Alpha / Composite Reliability), Pearson / Spearman correlational test etc.
Your initial analysis of Liker scalar data should not involve parametric statistics but should rely on the ordinal nature of the data. While Likert scale variables usually represent an underlying continuous measure, analysis of individual items should use parametric procedures only as a pilot analysis.
Prepare a simple grid to collate the data provided in the questionnaires. Design a simple coding system careful design of questions and the form that answers take can simplify this process considerably. Enter data on to the grid. Calculate the proportion of respondents answering for each category of each question.
Liker items are used to measure respondents' attitudes to a particular question or statement. To analyze the data it is usually coded as follows. One must recall that Likert-type data is ordinal data, i.e. we can only say that one score is higher than another, not the distance between the points.
Statistics Benefit the Group Who Collected the Information. The Sample Size Is Small. Error Margins Are Too Large. The Sample Representation Is Inaccurate or Biased. Incentives are Inappropriate for the Sample. The Context Is Not Reported. The Statistic Flies in the Face of Precedent.
Statistics Benefit the Group Who Collected the Information. The Sample Size Is Small. Error Margins Are Too Large. The Sample Representation Is Inaccurate or Biased. Incentives are Inappropriate for the Sample. The Context Is Not Reported. The Statistic Flies in the Face of Precedent.
For the individual, it's always all-or-nothing, but for the population, the estimates are still accurate. Statistical tools enable the analysis of results in research studies, so that when extrapolated to the larger population, those results are valid, helpful, and reliable.
When analysts use statistical procedures correctly, they tend to produce accurate results. In fact, statistical analyses account for uncertainty and error in the results. Statisticians ensure that all aspects of a study follow the appropriate methods to produce trustworthy results.
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