research methods: calculating measures of tendency & dispersion Flashcards
qualitative data is analysed using…
thematic analysis
what does research that gathers quantitative data produce?
numerical results called raw scores
what is a raw score table?
a table to show the individual self-ratings of two groups
what is a frequency table?
a table that is used to show how many times (the frequency) the scores occurred in the dataset
how can data of raw scores be summarised?
using descriptive analysis, measures of central tendency and dispersion
what is central tendency? what does it consist of
a descriptive statistic that calculates the average or most typical value in a dataset.
mean, median and mode
what is the mean? and when is it used?
- the mean is calculated by adding up all of the values in the dataset and dividing them by the number of scores collected
- it’s often used when interval/ratio level data is obtained. it is the most sensitive and most powerful measures of central tendency as all scores in the dataset are used in the calculation, but it can be affected by extreme values or when there is a skewed distribution
what is the mode? and when is it used?
- mode is calculated by look at the most frequent score in dataset. the mode is the value that occurs most frequently. if there are two frequent scores (bi-modal), both scores should be reported. if there are more than 2, then mode is meaningless measure of central tendency
- it is used when nominal data is obtained and easy to calculate. it is not affected by extreme scores, however it is not useful measure of central tendency on small datasets with frequently occurring same values
what is the median? and when is it used?
- median is calculated when values in dataset are placed in rank order (smallest to largest). when data set has odd number of scores, it is simply the middle score. if there is even number of scores, mean of 2 middle values need to be calculated
- used when ordinal level data is obtained. simple calculation to make and not affected by skewed distributions. however, it is less sensitive than mean and not useful on datasets that have small number of values as it may not represent typical score.
what is measures of dispersion? what does it consist of?
- a descriptive statistic that calculates spread of scores in dataset.
- range and standard deviation
what is the range? what may be the disadvantage of using range?
- the simplest calculation of dispersion. different between highest and lowest value. range is calculated by subtracting lowest value from highest value. high range value indicates scores are spread out and low range value indicates scores are closer together
- range is affected by extreme scores and may not be useful if there are outliers in dataset. also does not indicate if scores are bunched around mean score or more equally distributed around mean. I’d dataset does have extreme scores, it is better to calculate interquartile range: involves cutting out lowest quarter and highest quarter of values (top and bottom 25%) and calculating range of remaining middle half of scores.
what is standard deviation? what does it do?
- more useful way of investigating the spread of the scores is to use standard deviation. deviation refers to distance of each value from mean. e.g. if mean average rating for obedience by males is 7, and one male within group was 9, the deviation of score would be +2. if different male in group rated himself 5, then deviation value would be -2.
- each score in dataset would have deviation value, so to get single value that represents all deviation scores, standard deviation needs to be calculated. standard deviation gives single value that represents how scores are spread out around mean, higher the standard deviation, greater the spread of scores around mean value.
how can data be presented?
through summary table or graphical representation
what is a summary table?
it presents measures of central tendency and dispersion clearly
what is a graphical representation?
- graphs can be used to illustrate summary data or data frequencies. never illustrate raw scores in a graph, as this is bad practice. data should be shown that is meaningful and summative; raw scores do not show this.
