Data Report and Analysis Plan – Quantitative Template - Management Assignment Help

Assignment Task:

Task:

Data Report and Analysis Plan – Quantitative Template
In the box below please provide the Materials (and/or Apparatus) subsection from your method assignment. This content does not contribute to your word count, and is not directly assessed, but rather provides background for your marker to interpret your proposed analyses.
Background Information
The current study aims to examine how naming speed and planning skills moderate the relationship between subitizing performance and math ability in adults. It was hypothesised that: (H1) There will be a significant positive relationship between subitizing performance (IV) and math ability (DV) in adults. (H2) Strong naming speed (moderator 1) skills will positively increase the effect of the strength of the relationship between subitizing performance and math ability in adults. (H3) Strong planning skills (moderator 2) will also positively increase the effect of the strength of the relationship between subitizing performance and math ability in adults.
Note: All information that you add to the template after this point contributes to your word count.

Data Cleaning
In this section you will briefly explain and justify any actions you need to take in order to clean and prepare your dataset for your statistical/hypothesis testing. These are the steps you intend to take BEFORE your major statistical analyses. You should NOT be discussing specific assumption tests related to your hypotheses in this section. Some example points that you could discuss are listed below. Please note that this list may include elements not appropriate to your design; you will need to add/remove steps where necessary.
The importing of data from online survey software.
Cleaning data such as detecting erroneous entry errors or non-response.
Creating factor scores as per authors guidelines for validated scales.
Identification of missing data and considering what to do with missing values.
Preliminary exploration of data (e.g. descriptive analyses) which may be useful in detecting data related issues.
Note: You may also discuss early inspection of things like outliers and checking overall distributions however this should only be exploratory in nature – it is too early in the data cleaning process to be making decisions about things like multivariate outliers. Any recommended removal/changes of data related to outliers/influential points should be made with reference to your specific statistical tests (discussed in the assumption testing section below).
Data Cleaning
Please note the grey text is provided as an example. Please delete and replace with your data cleaning steps.
Step 1: Survey data will be exported from Survey Monkey and imported as .csv file into SPSS Version 25.
Step 2: Data will be visually inspected in both Data and Variable View in SPSS in order to check that string and numerical data has been imported correctly. All details about variables will be entered in the Variable View.
Step 3: A preliminary inspection of erroneous data will be made by using the … [explain how you will identify erroneous data]. I will identify missing and inaccurate data by … [explain how you will identify missing and inaccurate data]. I will … [explain what you will do with your missing and inaccurate data].
Step n: […continue adding steps until you are happy with your data cleaning and preparation process]

Analysis Plan
In this section you will:
Indicate the main statistical analysis that you intend to use for each hypothesis. Make sure your hypotheses reflect exactly what you intend to test for statistically. For example, if your analyses have covariates in it – these must be made clear in your hypothesis. All the variables in your statistical tests should be evident in your hypotheses.
Describe how the chosen statistic will appropriately test your hypothesis.
Describe the relevant assumptions for your chosen statistical analysis, how you will test the assumptions identified and comment on any other relevant considerations (e.g. Type 1 error, sample size, test reliability).
Further examples and advice are presented in the example table below. Please delete the example table before submitting your assignment.

Data Analysis Including Assumption Testing - Example
Hypothesis 1: State your first hypothesis here. e.g. “There is a significant association between gambling severity (problem vs non-problem gamblers) and type of gambling (slot machine, horse racing, card type games).”
[Note: The reader should be able to identify all your variables in your hypotheses and relate them to your statistical test below. If your hypothesis is multivariate in nature (e.g. multiple regression, ANCOVA etc.) then all variables should be represented in your hypotheses. If you have multiple, but separate hypotheses that use the same statistical test you can list them all here – just make sure this is clear in how you express your hypotheses (e.g. number them Hypothesis 1, 2 etc.).
Statistical test: List the statistical test you will use in order to examine your hypothesis (e.g. Multiple Regression, Moderation, Independent-samples t-test).
Type of data being used: State the names of all variables and level of measurement. e.g. “In Hypothesis 1, gambling severity and gambling type are both categorical nominal variables.”
[Note: every variable in your hypotheses must be described here, including covariates if appropriate].
How will the chosen analysis allow the hypothesis to be tested: Justify why/how the chosen analysis matches the hypothesis e.g. “The Chi-Square test of independence is an appropriate test for Hypothesis 1, as both variables are categorical in nature and the aim here is to compare the frequency distributions across each variable. This test will compare the observed versus expected cell frequency counts and allow any association to be tested statistically.”
Strengths and limitations: Describe some key strengths and limitations/weaknesses of using your chosen analysis to test your hypothesis. E.g. “The Chi-Square Test is useful for this simple bivariate hypothesis and results are easily interpreted by the reader. The Chi-Square Test is preferred here, over more complex logistic regression or log-linear modelling, as sophisticated statistics such as 95% CIs for odds ratios are not required to test this hypothesis or communicate results. Chi-Square Tests are robust to most assumption tests, however, are sensitive to small cell frequency counts, thus this will be a primary concern during assumption testing.
Other analytic considerations: Summarise any additional analytical considerations other than assumptions that you may need to consider whilst using this statistical model to test your hypothesis. E.g. “The Chi-Square Test of Independence will not provide the reader any measure of magnitude (size of the effect), therefore additional statistics such as Cramer’s V or odds ratios will need to be analysed and reported on.
[Note: You may choose to discuss other analytical considerations such as sample size (a priori or post hoc calculations) or bootstrapping here, rather than below in the assumption section. This is acceptable – so long as all analytical considerations are clearly justified.]
Testing assumptions: In the columns below, list each assumption relevant to the statistical test chosen above. Make sure you clearly define the assumption, explain how you will use it, explain what you will do instead if the assumption is violated, and any impact of this on your analyses/results.
Analysis assumption Definition of assumption. How will this be assessed? Please provide detail. What will be done if there are issues identified?
Sufficient sample-size and appropriate frequency distribution An expected frequency > 5 across 80% of cells. By visual inspection of my 3 x 2 (game type by problem gambling group) contingency table to ensure the expected frequency in each cell is at least 5 for all cells. Use Fisher’s exact test instead of the chi-square test as this uses the actual distribution of data to calculate the p-value rather than relying on an approximation based on the sampling distribution as the chi-square test does. Fisher’s exact test is more conservative than the Chi-Square Test (it produces relatively larger p-values) so power will be lost with this choice. That said, a process called ‘mid-p adjustment’ can overcome these issues (Lydersen, Fagerland & Laake, 2009), providing a similar p value to the Chi-Square Test.
A better option, if time permits, would be to collect more data until all frequencies are > 5 if possible. Alternatively, group levels of the variable game type could be collapsed together, in order to ensure adequate cell frequencies.
[Add further assumptions, one in each row] Note: Don’t forget to discuss all assumption tests for you chosen analyses, as well as consider issues like univariate and multivariate outliers/influential points. Finally, if you have multiple hypotheses that share the same assumptions, simply list the assumption name in the first table and refer the reader back to it as needed.
Note: Please copy and repeat the table for each hypothesis if a different analysis is required to test each hypothesis.
Data Analysis Including Assumption Testing
Hypothesis:
[Note: If you have more than one hypothesis, but the analysis you are using is the same for both, list both hypotheses here].
Statistical test:
Type of data being used:
How will the chosen analysis allow the hypothesis to be tested:
Strengths and limitations:

• Other analytic considerations:
• Analysis assumption Definition of assumption. How will this be assessed? Please provide detail. What will be done if there are issues identified?
• [Add further assumptions, one in each row] For mixed-method projects only – delete if not required: Please provide the additional details requested in the box below. If you have more than one qualitative research question, please repeat the box for each question if a different analysis is being used.
• Research question/s:
• [Note: If you have more than one research question, but the analysis you are using is the same for both, list both questions here].
• Proposed analysis:
• Type of data being used: (e.g. interview, open ended survey questions)
• How will the chosen analysis allow the research question to be tested:
• How will the qualitative analysis complement or inform your quantitative analysis outlined above:

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