Experimental Design and Analysis - Food Manufacturing Company - Statistics Assignment Help

Assignment Task:

Experimental Design and Analysis Assignment Help

TASK 1
For Task 1, analyse the data provided in each scenario 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 or an independent samples t-test (and because there are six scenarios, the same type of analysis might be applicable to more than one scenario).

As a first step, use the tutorials, e-topics, and the foolproof guide to help you write a draft results section incorporating your statistical findings. These results will contain
the necessary statistical values required to answer a list of subsequent questions. Specifically, in the draft results you should include;

(a) The hypothesis.

(b) The overall number of participants, and the means and standard deviations of the AGE of these participants, grouped by sex (Hint: if you find participants with missing
data, remove them completely from the data before you perform any further analysis, including age and frequency).

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

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.

Scenario 1
A large food manufacturing company seeks to determine the cleanliness attitudes of their 250 employees, compared to the general population. You are tasked to compare
the cleanliness attitudes of a sample of employees with the population cleanliness attitude. You measure employees’ responses using the Happiness at Work Scale, which validly measures work happiness. This scale provides a number from 1 to 50, where 1 refers to very low happiness, and 50 signifies extreme happiness. The population mean for “work happiness” on this scale is 25, but you don’t have access to the population standard deviation.

Your analysis will examine happiness differences between the company employees and the population. You predict the employees will show a higher work happiness score than the population.
You randomly recruit 30 employees from the 250, and they all agree to complete the Happiness at Work Scale at the organisation’s head office on a Friday morning. The data you collect from the 30 participants is shown in Table 1.

Task 1
(1) Write a results section (“draft answer”). 1 mark for this “working out”

(2) What is the name of the statistical analysis you used? 1 mark

(3) What is the mean age of male and female participants? 1 mark

Female =

Male =

(4) Which (if any) participant was an outlier? (Provide their ID number, or write “no outlier” if you didn’t identify anyone). 

(5) If there was an outlier, what number did you change their score to? (write “Not applicable” if there was no outlier).

(6) Was the assumption of normality met? (Yes/No). 

(7) What were the degrees of freedom for the test statistic? 

(8) What was the value of the test statistic? 

(9) What was the probability value associated with the test statistic? (use “p < 0.001” if the SPSS output shows “0.000” in the probability box). 

(10) What was the value of the Cohen’s d effect size? (write “Not applicable” if effect size is not relevant to the Scenario 1 analysis). 

(11) Based on the value of Cohen’s d you might have calculated, how would you describe the effect size?

TASK 2
Circle the number next to the statement which most accurately summarises the research findings. 

(1) The mean happiness rating of the sample was significantly higher than the population mean.

(2) The mean happiness rating of the sample was statistically equivalent to the population mean

(3) The mean happiness rating of the sample was significantly lower than the population mean.

(4) Not enough information provided to determine an answer.

TASK 3
You’ve mistakenly allowed a single, obvious design flaw to potentially affect the validity of your results. What is it? (only write a single word, phrase or short sentence; for
example, “lack of reliability” or “experimenter effect”).

Scenario 2
A health-food company is planning to advertise their (new) twelve-day weight-gain program to the public. The company executives believe the program will be effective in
helping people who have lost weight (due to various chronic illnesses) gain a healthy and sustainable weight in a short period of time. However, the company requires
scientific evidence that the program is successful, if they are to proceed with the marketing plan.

To help them gather this evidence, you compile a list of 177 patients who have been identified by medical professionals as having lost significant weight through disease
(which is in remission). You contact these patients and ask them whether they’d be interested in participating in your study, and if they agree they are to meet you in the
company’s training room on Friday morning so you can measure their weight (kg) and provide them with the diet program. This program consists of 36 pre-packaged meals that participants self-administer during the course of the study. 35 participants turn up to your meeting, and after weighing them you explain that the
program consists of eating three of the pre-packaged meals a day; morning, noon and evening for 12 days. They cannot eat anything else (to ensure control of meal portion sizes).

All 35 participants begin the program on Monday, which you label Monday Week 1. The diet regime ends on the Friday of Week 2 (12 consecutive days x 3 meals a day = 36 pre-packaged meals). All the patients are very enthusiastic and keep a diet diary for the fortnight, and you later determine that they all correctly followed instructions and ate all the meals as specified.

On the first Saturday morning after the twelve-day program ends (“Day 13”) you have a return meeting with the participants in the training room and measure their weight a second time. You now analyse the data from these participants to answer the company’s question – did the program result in a significant increase in mean weight for
the participant group?

TASK 1
(1) Write a results section (“draft answer”). 1 mark for this “working out”

(2) What is the name of the statistical analysis you used? 1 mark

(3) What are the standard deviations for the age of male and female participants? 

(4) Which (if any) participant was an outlier? (Provide their ID number, or write “no outlier” if you didn’t identify anyone). 

(5) If there was an outlier, what number did you change their score to? (write “Not applicable” if there was no outlier).

(6) Was the assumption of normality met for both sets of weight scores? (Yes/No). 1
mark

(7) What were the degrees of freedom for the test statistic? 1 mark

(8) What was the value of the test statistic? 1 mark

(9) What was the probability value associated with the test statistic? (use “p < 0.001” if the SPSS output shows “0.000” in the probability box). 

(10) What was the value of the r 2 effect size? (write “Not applicable” if effect size is not relevant to the Scenario 2 analysis). 

(11) Based on this r 2, how would you describe the effect size (small, medium, large or “Not applicable”)?

TASK 2
Circle the number next to the statement which most accurately summarises the research findings.

(1) Mean weight after the weight-gain program was significantly higher than mean weight before the weight-gain program.

(2) Mean weight after the weight-gain program was not significantly different to mean weight before the weight-gain program.

(3) Mean weight after the weight-gain program was significantly lower than mean weight before the weight-gain program.

(4) Not enough information provided to determine an answer.

TASK 3
You’ve mistakenly allowed a single, obvious design flaw to potentially affect the validity of your results. What is it? (write a single word, phrase or simple sentence only; for
example “lack of reliability” or “experimenter effect”).

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