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Statistical Analysis

Statistical Analysis. Is it just chance? Could it be a fluke?. Descriptive Statistics. Help us describe our data Do NOT allow us to draw conclusions about what our data mean…. Did the IV effect the DV? Mean – the average Median – the middle score Mode – the most frequently occurring score.

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Statistical Analysis

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  1. Statistical Analysis Is it just chance? Could it be a fluke?

  2. Descriptive Statistics • Help us describe our data • Do NOT allow us to draw conclusions about what our data mean…. Did the IV effect the DV? • Mean – the average • Median – the middle score • Mode – the most frequently occurring score

  3. Inferential Statistics • Allow us to draw inferences – that is to make conclusions • Allow us to decide whether or not any change in the DV was caused by the IV • Calculate the Probability of Chance • Could these results happen just by chance? • We want a very low probability of chance!

  4. Probability – the P value • P values are recorded as a decimal score • Eg. if P < 0.05 (5%) means that there is less than a 5% or 5 in 100 chance that the results of a study are just a fluke • As the probability of chance is very low we can assume that the results are meaningful – that is that the IV did in fact cause a change in the DV

  5. The Significance Booze Bus • For results to be considered significant the probability of chance must be below 5% In other words p < 0.05

  6. The t - test • The t-testis a mathematical procedure that involves a comparison of the means of two groups (or treatment conditions such as the presence or absence of the IV, or two different IVs). • A t-test allows a researcher to figure out if the difference between the experimental and control groups is big enough to be significant (that is not just a fluke) • So a t-test is just an example of a test you could use to assess the statistical significance of your results!! Criteria for applying a t-test • Only two groups are being compared for example: Experimental Vs Control group • A small sample of participants is used NOT and entire population • The spread of scores around the mean in each group is quite small

  7. Reliability and Validity Will it happen that way again? Are you really testing what you think you are testing?

  8. Reliability • Do we get the same results over and over again when using the same test? • Consistency, dependability, stability of test results • A ruler should not be stretchy! Otherwise I would get different measurements every time I measured things • Doing a personality test should yield similar results for the same person tested on different days • Internal consistency – do all questions on a test measure the same thing?

  9. Validity • Are you measuring what you think you are measuring? • Remember the Coke Vs Pepsi study? • This study was reliable in that it consistently showed that people preferred the letter Q to the letter M • It was however not valid as it was not measuring what it aimed to measure • Construct validity – when a measurement tool has evidence to suggest it relates to the behaviour being tested • If the VCE had construct validity it would correlate well with university performance and career success

  10. Validity • Internal validity – when a research design is appropriate to measure an specific behaviour • No major flaws in sampling, control of variables etc. • External validity – how far can the results be generalised beyond the research participants • Can other researchers replicate the study and its results with different samples and populations?

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