Quantitative Data Analysis P6 M4 Research Methods Quantitative Data Analysis P6 M4

Quantitative Data Analysis Numerical analysis of data is called statistics. They examine things like relationships or differences in data. They can be used to determine the answer to your research question or hypothesis. Relationships look at how one things effects another and can be analysed using correlations

Correlations Correlation is a measure of the relation between two or more variables. For example what is the relationship (correlation) between the number of times you exercise and your body fat Correlation coefficients can range from -1.00 to +1.00. The value of -1.00 represents a perfect negative correlation while a value of +1.00 represents a perfect positive correlation. A value of 0.00 represents a lack of correlation.

Pearson correlation (will now just be referred as correlation), assumes that the two variables are measured on at least interval scales and it determines the extent to which values of the two variables are "proportional" to each other. If the correlation coefficient is squared, then the resulting value will also give the strength of relationship R2

Task Using the data that has been given to you, transfer it to Excel Looking at the original data, what factors do you think relate to each other Test the correlation between the two factors using excel Put this information into a graph

P value- is what you are testing true The P value gives you an indication of what you are testing is true. In sports Science the common significance value is 0.05 This means that if your P value is below 0.05 (<0.05) then the results are statistically significant but if they are over 0.05 (>0.05) then they are not significant. Basically it is saying that 95 times out of 100 you would get this result. Anything less than this is not good enough and is too influenced by chance

Difference tests Difference test can be classified as Parametric Non-Parametric Parametric tests are used when the results are normally distributed and interval/ratio data Non Parametric Tests are used when the data is not normally distributed

the normal probability distribution describes the proportion of a population having a specific range of values for an attribute. Most members have amounts that are near the average; some have amounts that are farther away from the average; and some have amounts extremely distant from the average

The standard deviation of a sample is a measure of the spread of the sample from the mean. In a normal distribution, about 68% of a sample is within one standard deviation of the mean. About 95% is within two standard deviations. And about 99.7% is within three standard deviations. The numbers in this figure mark standard deviations from the mean.