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WorksheetsStatistical Methods 1- December quiz
Total questions: 10
Worksheet time: 10mins
A researcher is interested in seeing how caffeine affects reaction times in a Stroop task. Half of participants are given coffee before completing a Stroop test, and half are given water. This is an example of what experimental design?
Within subjects
Between subjects
Correlational
Mixed designs
A researcher wants to know how we react towards faces with different emotions. Participants' reaction times to press a button are recorded after they see happy, angry, and bored faces. What type of experimental design is this an example of?
Within subjects
Between subjects
Correlational
Mixed design
Which of the following is an assumption of the paired-samples t-test?
Data can be on a ratio, interval, or categorical scale
The sample of paired data is not random
The difference between scores is normally distributed
When graphing data, error bars must not overlap
A researcher wants to know if someone's levels of hygiene change when they attend a festival. Levels of hygiene on Day 1 versus Day 3 of Glastonbury festival were compared in a paired-samples t-test. What can we say about the data from the output above?
Our p value is equal to 0.
122 participants took part in the study.
There is no significant difference in hygiene scores between Day 1 and Day 3.
There is a significant difference in hygiene scores between Day 1 and Day 3 of the festival.
How does correlation differ from co-variance?
It standardises the units that are used in co-variance calculations.
Correlation values only show positive relationships between variables.
Correlations depend on the variability of variance scores.
SPSS cannot calculate co-variance values.
Which of the following is NOT true about Spearman's Rho correlations?
The can be used for ordinal data.
Data are ranked before applying Pearson's equation to the ranks.
SPSS can analyse multiple Spearman's Rho correaltions at the same time.
Data must be normally distributed for the test.
When conducting a multiple linear regression, we can visualise the data using:
Bar graphs
Plane of best fit
Line of best fit
Error bars
I want to see whether levels of anxiety about exams predicts their exam performance. I collect this data, put this into SPSS, and it gives me a summary of my model. From the table above, how much variance in exam scores can we say is predicted by exam anxiety?
4.41%
44.1%
1.94%
19.4%
I am interested in seeing whether Facebook advertising boosts sales of a product I am selling. After a week of posting Facebook adverts, there is no significant increase in sales, so I take down the ads. However, a month later I hear that a rival company has used Facebook ads and seen a large increase in product sales.
What type of error did I make?
A Type I error
A Type II error
No errors were made, and my results were accurate
There is no way to tell from this information
Which of the following does NOT impact the power of a study?
Number of participants
Chosen alpha level
Choice of study design
The amount of time taken to collect data
