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A prediction of the relationship between academic achievement in the recent grades and selfregulation, self efficacy, locus of control and conscientiousness among students in higher education. ( my suggestion_)
Rationale (300 words)
You will need a rationale for your study
Outline prior research in this area ~ 34 relevant similar studies
Build the rationale:
Why do we need this research
How does it connect with past research
State your hypotheses
This is only 300 words so you won’t be able to cover in detail the questionnaires we used, or the reasons for choosing them
Say something about the questionnaires and the demographics …
… focus on the items that were correlated with the recent.grades
Measures (200 words)
You are not asked to write a full methods section in your submitted report. There will not be the word count.
The focus here is demonstrating your ability to summarise the measures used in your data analysis using a clear, conventional approach
Again, mention the questionnaires and the demographics
… but focus on the items correlated with recent.grades
Results (600 words)
Clearly, this is the major part of your lab report
Please cover these elements:
Summarise your sample (gender, age, who were they)
Data diagnostics (Normality? Missing data? Out of range? Peps excluded? Multicollinearity for the multiple regression?)
Descriptive statistics (mean and SD or frequency of your demographics, e.g., gender, educational level, number of dependents)
Reliability analysis = Cronbach’s alpha for each scale
Correlations – please present a table of correlations
Multiple regression
Correlations table
Mean (SD)  Edu. level 
No. depend 
Age  Easy. time 
Support. closest 

Recent.grades  –  0.33 *  0.28 *  
Edu.level  –  –  
No.depend  Yes  –  
Age  Yes  –  
Easy.time  Yes  –  
Support.closest  Yes  – 
* = significant at 0.05; ** = significant at 0.01; *** = significant at 0.005
Table 1: correlations among the items that significantly predicted recent grades (N=90). Easy.time and support.closest were measured on a scale of 1 to 5.
Reporting the multiple regression
Lots of output with multiple regression! Aim to present a selective, coherent analysis, reporting relevant statistics.
The R² value: This tells us the proportion of variance in the outcome variable explained by the full set of predictors
The p value of the Anova: This tells us whether or not the predictor variables, taken as a set, significantly predict the outcome variable
The p value of each predictor: this tells us whether each predictor significantly predicts the outcome variable
The standardised coefficients (ß values): comparison of ß values tells us which explains most variance in the outcome variable
Multiple regression table
Table 2: Say what the table shows. Say how many pcps. Say what * signifies
Discussion and limitations (400 words)
Start with a summary of your results in English (no numbers or stat tests)
Interpretation – do what you can in the word count.
Can you explain why each of the items were significant predictors?
Can you explain why the questionnaires were not sig predictors?
Refer back to the previous studies from the Intro – how do your results compare?
Are there any major limitations to the research?
Please explain why they mattered. It’s not enough to just say there were more women than men, you have to say how this could have affected the results
Don’t take a scattergun approach and critique everything you can think of!
Ideas for future research
Style
Please prepare this report using only the section headings outlined above.
The total word count is 1500 words (+/10%), excluding references and appendices.
Section word counts are flexible/suggested, but reflect section weighting in the marking
Your submission should include your SPSS output demonstrating all analyses run that are relevant to your report writeup (in appendices, not part of your word count ).
Please use double spacing, font size 12, and paragraphs.
Always aim to write in your own words. This will help you to develop your understanding of material and will help you produce stronger work (and avoid plagiarism).
Aim to write in a concise and clear way without being overly technical in tone.
Please use third person tense (e.g. “It was decided…”) rather than first person (e.g. “I decided…”).
The word count is tight for this Lab report. Aim to cite a small number of references.
Focus on searching the academic databases, rather than general information from the Internet. You are expected to be using primary research.
Sections of a Quantitative Lab Report
Title (The title is not included in the word count.)
Add a ‘research findings’ type of title that reflects details of your methods, your sample, and your findings. A rather general example might be: “A survey of personality traits and psychological beliefs as predictors of health eating among British university students”
Rationale (300 words)
Outline prior research in this area referring to around 34 relevant similar studies. Then, build your study rationale: explain why this research is needed and how it connects with past research. Conclude this section with at least one hypothesis.
Measures (200 words)
You are not required to write a full methods section in your submitted report. There will not be space! The focus here is demonstrating your ability to summarise the measures used in your data analysis using a clear, conventional approach.
Results (600 words)
Begin with a very brief summary account of your sample, providing details of (for example) the sample age range. You should then proceed to provide an account of your reliability analyses to give a sense of the internal consistency of each scale. Then summarise output showing inferential data analyses and findings, perhaps starting with correlational analysis. Your report should then contain a statement concerning data diagnostics (e.g. assumed absence of multicollinearity) giving a sense of whether/how your data met the parametric assumptions of tests you will be using. Your diagnostics account leads into the heart of your report – providing a complete and coherent account of the multiple regression analysis/analyses presented in the output. Your account should take the reader through the different tests you used, and what statistical output from these tests revealed.
Discussion (300 words)
Restate the focus of the study (e.g. what variables were involved) and summarise your study findings. Then, link these findings to 34 relevant records/articles (your selection of articles can include studies discussed in your rationale section but can also include other relevant articles/studies).
Limitations and extensions (100 words)
Discuss limitations of your methods and how the study/analyses could be extended in further quantitative or qualitative empirical work.
References (not included in word count)
Any references should be included in correct APA style (American Psychological Association) at the end of your report.
– decisions for lab report 1 – additional support
Whether to include all 4 standard questionnaires or only some of the 4  All 4 questionnaires
They were each predicted to have a relationship with the target variable and should be included for this reason (we generally report the results of our predictions). It is important to show which variables were investigated that did not have a relationship with the target, as well as those that did. 
A subset
You could include only the 2 or 3 most theoretically relevant (based on your reading). There is some duplication among the questionnaires so you could examine the correlations among the 4 questionnaires to decide which can be dropped. 
Which pcps to include  All of them.
In order to do this you will have to replace missing data with the mean for the variable. Pros: Uses all the data you have. The sample size is not large, so it is attractive to retain as many pcps as possible. Cons: pcps with missing data may not have been paying strong attention, so perhaps you could exclude them for this reason. 
Exclude those with missing data
The number of pcps you include in your analysis will depend on which variables you analyse, as different variables have different missing data. Pros: You are only including real data. You are excluding pcps who may not have been paying attention. Cons: You have a smaller sample size Detail: there were 97 pcps altogether. Four were originally marked as all present = 0. 
How to show descriptive data  Means
Pros: The simplest and uses fewer words. Cons: doesn’t make sense for some variables (e.g., gender). Doesn’t capture the full information. 
Frequencies
e.g., the distribution of highest level of education was as follows: x% had BA/BSc, y% had MA/MSc, and z% had PhD or professional qualification. Pros: this is more meaningful, and shows the distribution of the data Cons: uses more word count 
Which variables to include in the correlations  All of them
Pros: completeness. It is important to show which variables did not have a relationship with the target variable as well as those which did Cons: you don’t have the word count. Correlation table would be too large. 
A subset
Pros: manageable in terms of the word count and the correlation table. Cons: not a complete presentation of the research, but this is acceptable and understandable in terms of the word count. 
Which variables to include in the MR  Only those with a sig (or near sig) bivariate correlation with the target
Pros: a smaller set of variables, and easy to justify their inclusion. Cons: will miss out on a variable that was sig in the MR but not in the correlations. Will exclude variables with strong theoretical significance. 
The set I showed you in lectures
Edu.level, no. depend, easy.time, support.closest, age Pros: these are all justifiable in terms of their bivariate correlation or in terms of their theoretical importance. Cons: some of these don’t have a significant bivariate correlation, but they all have theoretical significance. 
How to report the MR  In a table
Pros: this is a standard approach. Saves word count. The text paragraph can then merely draw attention to the important data in the table. Cons: you will need to learn how to lay out a table (please note you have been given two examples, one in the lecture and one in last year’s example lab report). 
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