• Please see the assessment brief below – this gives an outline of all the instructions for the assessment including the set questions, the total marks available for reporting the analyses for each section and the total marks available for the questions requiring interpretation. The assessment brief indicates the relevant questions that require the reporting of statistics and those that include interpretation and additional reading. Please follow guidelines provided in lectures and ensure you follow APA guidelines.
• In the assessment, we are looking for learners to provide an accurate reporting of the relevant statistics (e.g., t-tests, Factorial ANOVA, Tukey test and hierarchical linear regression). We are also looking for a clear interpretation of the results for the questions that ask about interpretation (see assessment brief). You will need to present Tables and Figures in line with APA guidelines along with references where indicated.
• The module handbook and the separate assessment brief indicate the percentage weighting for this work (40% of the module grade). Please see the separate assessment brief for marking criteria – marks are provided for accurate succinct reporting of statistics, presentation of Tables/Figure, a clear interpretation of the findings for the relevant questions, and engagement with relevant literature to support the relevant questions. Please see the assessment brief for a breakdown of the marks available for reporting and interpreting the results for each section – this shows the specific questions that require accurate reporting of statistics (in the correct format) and those that require understanding by interpreting the results.
• The assessment tests the following learning outcomes:
• Enter and manipulate data, perform a range of statistical analyses and graphically illustrate statistical output using SPSS for windows (Learning Outcome 2).
• Demonstrate an understanding of the rationale for, and appropriateness of, the univariate and multivariate analytical techniques introduced in the module by selecting the appropriate analyses for different areas of application (Learning Outcome 3).
• Interpret the output of SPSS analyses introduced in the module and report the results appropriately (Learning Outcome 4).
• Demonstrate a technical and critical knowledge of methodologies introduced in the module, such as: qualitative; longitudinal; structural equation modelling and regression techniques (Learning Outcome 5).
• In order to prepare for the assessment, you should have engaged with all the lecture materials, workshop handouts and practice tasks provided including the interactive tasks used in lectures (e.g., tasks related to t- tests, ANOVA, interactions, calculating Tukey test and regression analyses).
• The lectures/workshops relating to t-tests, ANOVA, interpreting interactions, and regression analysis all support this assessment.
• Please see Moodle for suggested reading related to the topic/assessment.
Please include the appropriate cover sheet with your assessment – this is available on Blackboard. You should format the assessment using 1.5 line spacing and either Times New Roman or Arial font (12 point). Each question should be clearly numbered for both sections and where word counts are indicated on the assessment brief these should be followed. There are no formal penalties for exceeding a word count of a few words (e.g., up to 20 words over is fine), yet if answers exceed word counts substantially then marks are likely to be reduced as this may reflect reporting that is not succinct and may contain irrelevant information indicative of lack of understanding. Please follow APA guidelines for referencing studies and for formatting tables and figures. You should submit your work to Blackboard electronically using the Turnitin link provided on the module space.
• You will find links to lots of useful resources in the My Library tab on Blackboard.
• If you have not yet made the college aware of any disability, specific learning difficulty, long-term health or mental health condition, please contact e-aitima@icps.edu.gr.
• If you have any other query or require further support you can contact the module leader: c.fountoulakis@icps.edu.gr.
• If you have any valid mitigating circumstances that mean you cannot meet an assessment submission deadline and you wish to request an extension, you will need to contact e-aitima@icps.edu.gr prior to the deadline.
The assignment comprises two sections and assesses your understanding of quantitative analyses (e.g. ANOVA & hierarchical regression). Please try to answer all questions – some of these can be answered based on lecture materials and others require additional reading. Engaging in additional reading will help to consolidate your understanding regarding this assignment. Support your answers with references wherever possible. When presenting your portfolio please label the two sections as indicated above and include the respective numbers for questions. Present all your answers in a narrative – do not use bullet points otherwise marks will be lost for that question. It is important to organise your time effectively in completing this work to allow you to meet the deadline – and to gain support from staff during academic support hours and taught sessions where needed. The questions will be easier to address once you have engaged with the relevant lectures, the workshop and lecture tasks provided as well as additional reading. You can use the taught sessions to raise questions that you may have about aspects of the portfolio.
1. The SPSS data on blackboard are from a study that examined sentence reading times. Sentences were either long (15 words per sentence) or short (8 words per sentence). The second factor was space information – sentences either contained a space between each word or the spaces were removed between the words. Sentence reading times were recorded in milliseconds. Participants read the sentences under all conditions – two sleep groups took part in the study (good sleep quality group versus poor sleep quality group). Conduct a three-way Factorial ANOVA on the data provided (see Blackboard for SPSS data file) and produce an APA formatted table of means and standard deviations showing the values for all levels of all factors. Include a table number and a meaningful title and show consistency in reporting decimal places. You can round up to two decimal places if you prefer or report to three decimal places. See the example table on the lecture slides and Table 1 on this handout for format. [100 words].
2. Report the results from the ANOVA you have conducted in a succinct narrative. Describe the ANOVA conducted using the correct design notation and make it clear whether the factors are between or within- participant factors. You must report the results for all effects (3 main effects and 4 interactions) and ensure that they are all accurate to enable you able to gain maximum marks for this question. Report the F ratio, degrees of freedom, MSE, p value and effect size for all effects. Marks will be lost if reported as bullet points – check lecture slides if you are unsure about the reporting. Please follow convention when reporting p values – report p as either p < .001 (less than .001) or state the exact p value. [250 words].
3. Introduce and present a table of means and means differences to show the descriptive information for the sentence length x space information interaction. Follow the format and example used in the lecture (e.g., arrange means from lowest to highest along vertical and horizontal axis; compute mean differences for the comparisons involved in the interaction). Conduct the Tukey HSD test by hand showing your calculations in an Appendix and use this information to indicate the significant comparisons in your table of means/mean differences (use an asterisk if they reach significance). Report the results of your comparisons – you should refer to each comparison along with the correct Tukey and p value for each effect. You should interpret the direction of effects. [150 words].
The SPSS data file on Blackboard includes variables from a study on impulsivity (criterion) measured using a self-report inventory. Several predictors of impulsivity were explored including age, polydrug use, emotion regulation, working memory and problem gambling behaviour (see Appendix A). Participants also completed a measure of full-scale IQ.
1. Conduct a hierarchical multiple regression analysis to explore the impact of the predictors on impulsivity. Enter impulsivity as the criterion, and on step 1 enter full-scale IQ and polydrug use as predictors. On step 2, enter age and emotion regulation as predictors and then enter working memory and problem gambling as predictors on your final step (step 3). You must enter the predictors as stated. Report the results in a narrative (this should include an introduction to the analysis – stating the criterion and predictors entered). Ensure that you report the results from the ANOVA model for all steps of the analysis and comment on the percentage of variance explained as well as the F change statistics and extra variance explained on subsequent steps. Include the beta values for all models. [400 words].
2. Present an APA formatted table to show the B, SE of B and Beta values for your data. Remember to number the table and to provide an appropriate/meaningful table title. [150 words].
3. Provide an interpretation of your results – explain what beta values represent and interpret the beta values for your results for all steps of the analysis (see lecture slides). What do the beta values show for this study? [250 words].
4. Supporting your answer with references, provide a brief discussion of the influence that residuals play in multiple regression analysis and the extent to which they may compromise the integrity of the results. [300 words].
5. Explain the difference between mediation and moderation analysis and outline the type of research questions these analyses allow you to address. You should support your answer with references. [300 words].
|
Variable Name |
Measure |
Score |
|
Impulsivity(criterion) |
Self-report inventory |
High score = greater impulsivity |
|
Age (predictor) |
|
|
|
Polydrug use (predictor) |
Self-report |
Higher score shows more incidents of drug use |
|
Emotion regulation (predictor) |
Self-report |
Higher score shows better emotion regulation |
|
Problem gambling (predictor) |
Self-report |
Higher score shows more evidence of problem gambling |
|
Working Memory (predictor) |
Digit Span (forward/backward) |
Accuracy score = higher score shows better performance |
|
Full-Scale IQ |
WASI |
Standard score |
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