Survey research and design in psychology/Tutorials/Introduction/Getting started: Difference between revisions
Update for 2018 |
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# Login to a computer |
# Login to a computer |
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# Launch an internet browser (Google Chrome is recommended) |
# Launch an internet browser (Google Chrome is recommended) |
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## Go to the {{:Survey research and design in psychology/Web resources/ |
## Go to the {{:Survey research and design in psychology/Web resources/UCLearn}} site for the unit |
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## Navigate to the [[../../|tutorial]] notes |
## Navigate to the [[../../|tutorial]] notes |
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# Launch SPSS (click on the Start button and search for SPSS) |
# Launch SPSS (click on the Start button and search for SPSS) |
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# Launch and test the Virtual Tutorial from the |
# Launch and test the Virtual Tutorial from the UCLearn site (if doing an online tutorial) |
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If using a laptop, you will need: |
If using a laptop, you will need: |
Revision as of 05:02, 12 February 2018
Set-up for tutorials
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View the accompanying screencast: [1] |
In order to get started on time, please arrive to tutorials at least 5 mins early in order to:
- Login to a computer
- Launch an internet browser (Google Chrome is recommended)
- Launch SPSS (click on the Start button and search for SPSS)
- Launch and test the Virtual Tutorial from the UCLearn site (if doing an online tutorial)
If using a laptop, you will need:
- SPSS - Can be purchased online via IBM SPSS Buy now
- UC-wifi access
What will be covered
Week | Tutorial | Title | Key content |
---|---|---|---|
01, 02
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01
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Using SPSS and APA style
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03, 04
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02
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Non-parametric and Product-moment
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05, 06
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03
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Factor analysis and reliability analysis
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09, 10
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04
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Assumptions and steps
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12
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05
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Open lab space to work on report
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Recordings
- Blackboard Ultra recordings are made of the Virtual Tutorials - links to these recordings are available on the UC LearnOnline Moodle site for the unit and each of the tutorial start pages.
- Screencasts are also available for each tutorial.
Assumed knowledge
The following aspects of using SPSS are assumed knowledge. Use of SPSS is readily forgotten, so it is recommended that you refamiliarise yourself with each of the following:
- Data editor
- Data view
- Variable view
- Creating a data file
- Saving and opening a data file
- Working with data
- Insert and delete cases and variables
- Sorting cases
- Compute new variables
- Recoding variables
- Split file
- Select cases
- Summarising and displaying data
- Frequencies
- Descriptives
- Explore
- Chart builder
- t-tests and ANOVAs
- One-sample t-test
- Independent samples t-test
- Paired samples t-test
- One-way between-groups ANOVA
- One-way repeated measures ANOVA
- Factorial ANOVA
- Mixed design ANOVA
- ANCOVA
To succeed in this unit, you will need to become a confident SPSS user. Please make sure you know how to create, save, close and open data, output, and syntax files and how to manipulate spss files, such as add/delete variables and cases, sort data, add variable labels and value labels, and recode data.
Good ways to revise SPSS skills include:
- use the in-built SPSS tutorials (via SPSS - Help - Tutorials)
- work through any of the variety of SPSS how-to guides that cover these basic SPSS skills.
It is strongly recommended that you commit at least two additional hours to prepare for an upcoming tutorial and/or to repeat a previous tutorial.
Golden rules
![](http://upload.wikimedia.org/wikipedia/commons/thumb/0/00/MUTCD_D9-16.svg/110px-MUTCD_D9-16.svg.png)
- "There is no thing as a dumb question." - Carl Sagan. If you are thinking of asking a question, chances are that others are wondering the same thing, so be brave and ask. Asking questions helps to make learning more interactive.
- If you fall off the back of the truck (whilst doing data analysis exercises), then put your hand up immediately and ask for a step to be repeated or for individual help as soon as possible. It is much more efficient to help you get caught up when you're only a little bit behind, than to try to help you catch up once you are many steps behind. It is important that you actively complete each of the data analysis exercises rather than just watching demos. Actively completing the exercises will lead to deeper learning.