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Assessment 1 (Autumn Semester 2022)

Assessment 1 contains THREE research scenarios. For each scenario, you are assuming the role of a researcher (employed by various commercial and government organisations) for the purpose of helping them find solutions to questions they have about their business practice.

Each scenario is worth 20 marks (meaning the overall assessment is out of 60, which is then divided by 2 to total 30% of the unit’s mark).

Each scenario consists of THREE tasks, detailed as follows;

TASK 1: Draft results and associated questions

For Task 1 in each scenario, analyse the data provided in the table using the relevant statistical technique (using SPSS, or perhaps even “by hand” if a z-test is appropriate).

Read the scenario carefully – the analysis required will be either a one-sample z-test, a one sample t-test, a dependent samples t-test, an independent samples t-test or a one-way ANOVA, or their non-parametric equivalents (and because there are only three scenarios, some of the analysis styles you’ve learned about will not be applicable to this assessment).

As a first step, use the e-tutorial demonstration exercises, e-topics, and the relevant “results” exemplar in the foolproof guide (e.g., for an independent samples t-test) to help you write a DRAFT results section incorporating your statistical findings. This draft results section will contain the necessary statistical values required to answer subsequent specific questions (and can be anywhere between 150 and 300 words, depending on the analysis you use; simply use as many words as you require to successfully complete the draft based on the class exemplar).

In the draft results you should include;

(a) An opening sentence describing the hypothesis.

(b) Participant number, mean age and standard deviation of age.

Make sure you provide the number of participants IN EACH GROUP. This is straightforward if you are performing a z-test, one-sample t-test, dependent samples t-test, or Wilcoxon T-test, because there is only one group of participants. You should report the number of females and males for each group and calculate means and standard deviations of the age of males and females separately for each group.

However, be aware there will be two groups for an independent samples t-test or Mann-Whitney U test, and three or more groups for ANOVA or Kruskal-Wallis.

So, for example, if the scenario involves two groups of participants (i.e., two levels of the IV, for example an independent samples t-test), for GROUP 1 calculate the number of females, their mean age and standard deviation; then calculate the number of males, their mean age and standard deviation. Then for GROUP 2, calculate the number of females, their mean age and standard deviation and separately the number of males, their mean age and standard deviation.

(Hint: if you find participants with missing data, remove them completely from the data before you perform any analysis, including mean age and number of participants).

(c) Tests of parametric assumptions (outliers and normality)

OUTLIERS

Investigate outliers separately for each group, if you identify a “between” design is used. For a “within” design, investigate outliers for each level of the independent variable (e.g., if it is a “before-and-after” repeated measures design, test outliers separately for the before data set, and for the after data set).

If you do discover (and then change) an outlier for a data set, you do not have to re-run the outlier analysis again to find more. One analysis is enough!

NORMALITY

For one-sample z and t-tests, there’s only one column of data for the single group, so you only need to perform a single normality test.

For a dependent samples t-test involving two columns of data for each participant, examine normality for each column separately.

Test normality separately for each group. That is, if the scenario has two groups (an independent samples t-test or Mann-Whitney U test), then test normality for each of the two groups. If the scenario has three or more groups (e.g., a between-subjects ANOVA design) then test normality separately for each group.

(d) The appropriate statistical findings (e.g., t-test results)

(e) A brief interpretation of the statistical findings

(f) Effect sizes and confidence intervals (only include for a scenario if appropriate, i.e., if it is something we include in a results section in the relevant tutorial demonstration exercise).

Following the draft results, there is a list of additional questions to answer. Once you have written the draft results section, the additional questions should be straightforward to answer.

TASK 2: Describe your research findings.

For this task you will need to choose the SINGLE correct statement (from four provided) which best describes your statistical findings.

TASK 3: Identify a design flaw in the scenario.

Each scenario has a major experimental design flaw, potentially affecting your statistical finding and leading to an inaccurate or invalid conclusion. For this task, name this flaw as a single word, phrase or single sentence ONLY.

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