# Survey research and design in psychology/Assessment/Lab reports/4
### Lab report 4: Multiple linear regression

Task: Conduct and report on a multiple linear regression which uses at least three independent variables to predict a dependent variable.

#### Marking criteria

In addition to the generic guidelines, this report should include:
* 5%. Title/Abstract: : Provide a succint overview of the study.
** Sumarise the study's purpose, method, findings, and implications in less than 150 words.
* 10%. Introduction:
** 5%. Background: Briefly introduce the topic and background literature about the research question and/or hypotheses.
** 5%. Research questions/Hypotheses: Propose logically-derived research question(s) and/or hypotheses (which is/are addressed in the Results). Research questions could be in form of "To what extent do A, B, and C predict the variance in Y?). Hypotheses could be in the form of "It is hypothesised that A, B, and C will each be positive linear predictors of Y").
* 10%. Method:
** Participants: N/A
** 10%. Materials: Explain the measures used for the variables in the current study, including summarising how any composite scores were derived and their internal consistency.
** Procedure: N/A
* 50%. Results: Describe and present the results of a multiple linear regression analysis involving three of more independent variables. Reporting should summarise any data screening and/or recoding/dummy coding (if not already covered in Materials), type of analysis, assumption testing, correlations between the variables, variance explained, and regression coefficients (including B, Beta, t, p, and sr$^2$). No figures are required. The results should demonstrate a clear understanding of the nature of the linear relationships amongst the variables.
* 25%. Discussion: Explain what the analysis found out about the research question(s) and/or hypotheses e.g.,
** How much variance was explained in DV? Why?
** Which variable(s) contribute most/least to understanding the DV? Why?
** What were the study's strengths and weaknesses? What could be improved?
** What conclusions can be drawn and what are the implications?

#### FAQ
##### Should I use items or composite scores?

* Either
* If you're creating composite scores without having done an exploratory factor analysis first, then at least conduct reliability analysis and report this as part of the Method
* Well-derived composite scores (where available/possible) will generally provide for a more valid measure of fuzzy constructs than single-item scores, but this will depend on the specifics of the construct being measured.

##### Is my R$^2$ big enough?

* Size of R$^2$ doesn't matter in terms of meeting the marking criteria. In fact, R$^2$ of 0 is acceptable. This is not an exercise in coming up with a large R$^2$ , but rather one in which a theoretical model is tested to determine the extent to which a set of IVs predict a DV. Many models explain much less than 30% of variance. Although we might like to explain 30%, the reality is often much less. It might be just as important to know that some IVs don't predict a DV. This also gives you something to consider in the Discussion - how could the model and/or measures be improved?

##### Are my predictors significant enough or strong enough?

* The aim is conduct an MLR to examine how well some IVs predict a DV. Thus, it is not necessary to seek only significant predictors. It's more important to have a rationale for a model and then to test and interpret the results, regardless of their significance or size of effect.

##### What results should be presented in a table?

* The correlations and regression coefficients are best summarised in a table.
* For examples, see the MLR sample write-ups e.g., Table 12 from the sample report and Table 2 from the MLR report are good examples of tables which show the correlations and coefficients for a multiple linear regression analysis.

#### General feedback

Overall, performance was slightly better than on previous reports (67.9%).
* Word count
** Many assignments went over the maximum word count and thus received penalties
* Formatting
** Use page breaks rather than multiple line spaces to separate pages (better layout)
* APA style
** Formatting of citations was much better than previously. There is still some room for improvement - see previous feedback for suggestions.
** More reports used Australian spelling - see previous feedback for suggestions.
** Numbers were still often not formatted correctly - see previous feedback for suggestions.
** Spaces should be used either side of symbols which replace words (e.g., p < .05 instead of p<.05)
* Title
** Some titles lacked sufficient detail e.g., "factors predicting..." is vague - which factors?
* Abstract
** Generally, well done.
** The weakest aspect was too information about the design and not enough detail about the results e.g., for each predictor. Explain the significant, strength, and direction of the tested relationships.
** Avoid statistics in Abstract unless particularly pertinent.
* Introduction
** Literature reviews are becoming more succint and focused towards research questions - good to see.
** Research questions were generally clear and appropriate.
* Method - Materials
** Method sections are becoming more succint with important summary details
** Provide a citation to the survey and explain each of the items/composite scores and measurement scales etc. If using composite scores, report the number of items and internal consistencies.
* Results
** Generally, well done.
** Assumptions were well tested and described.
** Correlations sometimes were not included in a table and summarised.
** A table of regression coefficients was generally well presented, with effective description of the overall model.
** There was often a lack of sufficient explanation of the results for each of the predictors.
* Discussion
** Generally, well done.
** Stronger recommendations are those which are more specific and insightful; vague, unrelated recommendations are not convincing.
* References
** APA style in this section is improving.
** Do not include issue numbers for seriated journals (which is most of them).
** Journal article titles should be have the first letters capitalised.