Data Analysis- What do I need to know? What are…. Levels of measurement Measures of central tendency (mean, median, mode) Measures of dispersion (range, standard deviation) Histograms Bar Charts Scattergraphs

Levels of Measurement In psychology we use three levels of measurement. The levels of measurement are called nominal, ordinal, and interval. NOMINAL When we make observations and put them in categories we are using a nominal scale of measurement.

Nominal data provides less than other levels, and is therefore sometimes called the lowest level of measurement.

ORDINAL Ordinal data tells us about positions or ranks within a group. It involves ranking scores, that is, putting them in order of increasing (or decreasing) size. It does not give us any information about the distances between those positions.

Example The scores of ten children on an intelligence test were put in order, from highest to lowest: A4thF8th B1stG3rd C9thH6th D2ndI7th E10thJ5th Ordinal data provides more information than nominal data.

INTERVAL Interval data measures in equal, agreed units. Examples are minutes, kilometres, number of words recalled. An essential feature is that intervals on the scale are equal. For example, the difference between 3 and 6 minutes is the same as that between 7 and 10 minutes. Interval data provides the most information, and is called the "highest" level of measurement.

Measures of Central Tendency You will be familiar with the idea of an average. There are, in fact, three different types of average, known as 'measures of central tendency'. l. The Mean: this is the score that we usually refer to as an average. It is calculated by adding all the scores together, then dividing by the total number of scores. The mean is valuable to the psychologist because it takes all the data into account and it can be used in further statistical analyses. 2. The Median: this is the halfway point that separates the lower 50% of scores from the higher 50%. The median is especially useful when there are a few extremely high or extremely low scores which can give a misleading average score. eg. six scores on a test out of 100 are: 70,74,75,77,78,100. The mean, or average score, is 79 but this is misleading in the sense that only one of the six Ps has scored this high. The median score of 76 is a better description of the data.

3.The Mode: this is the score that applies to the greatest number of pps. Eg. with scores of 30, 30, 30, 50, 96, 100 the average is 61 which is misleading in the sense that no-one scored anywhere near this; the median is 40, which again does not approximate to anyone's score and the mode is 30, which at least lets us know that more people obtained this score than any other score. The mode is useful in certain instances where other measures of central tendency are rather meaningless. For instance, if you are a buyer for a shop whose target population consists of 50% of people who wear size 12 clothes and the remaining 50% are size 16 then there is no use you ordering size 14 clothes just because this is the average size! In Milgram's experiment on obedience, it is more useful for us to know the voltage which the largest number of people went to (mode) than to have a mean or median score. Nevertheless, modes are used less often than other measures of central tendency. They do not tell us anything about other scores in the distribution; they often are not very ‘central’ and they tend to fluctuate from one random sample of a population to another more often than either the median or the mean.

Measures of Dispersion This measure gives an indication of the spread of scores (the extent to which the scores deviate from the measure of central tendency). The Range: The simplest of the measures as it is the difference between the highest and lowest score. + Easy to calculate + Takes into account extreme values -Can be greatly influenced by one score that is different form all the others Standard Deviation: It calculates the average distance from the mean of all scores. It is a more powerful measure of dispersion than the range since the values of ALL scores are considered. + Takes into account ALL scores -Much harder to work out, more time consuming

Graphs and Charts Histograms These use bars which touch and are used with interval data only. They must also be CONTINUOUS (i.e. no breaks & start at 0!).

Bar Charts These use bars which do not touch and can be used with all types of data. However, they are best suited to nominal data (discrete categories) as they only show you what falls into each category.

Correlational Analysis A correlation can show you if there is a LINK between 2 factors. E.g. height & shoe size. This link can be placed on a continuum, ranging from: _________________________ None Weak Strong Types of correlation: Positive- As 1 factor goes up, so does the other. E.g. Taller people have bigger feet. Negative- As 1 factor goes up, the other goes down. E.g. Shorter people have bigger feet/taller people have smaller feet. None-The factors don’t appear to go in any direction at all!

Scattergraphs The graph used to show that there is a correlation (link) between 2 factors is called a SCATTERGRAM. NegativePositiveNone

Correlation Co-efficient This shows you how closely 2 factors (height & shoe size) are linked. This ranges from 0 (no correlation) to 1 (very strong/perfect correlation). A 0.00 number suggests that there is NO correlation between height & shoe size. A – number suggests that there is a NEGATIVE correlation between height & shoe size. A + number suggests that there is a POSITIVE correlation between height & shoe size.

Analysis of Qualitative Data Qualitative data tends to be more descriptive and detailed than the more numerical quantitative data. Qualitative data can be collected from Observations Questionnaire surveys Interviews

This type of data can take many forms, for example - written records of events, e.g. field notes or interview transcripts - video or audio recordings - coding or check lists - behaviour traces, e.g. degree of littering This sort of data can be analysed in a qualitative or a quantitative manner. In many studies it may be analysed in both ways but this can depend on the nature of the data collected.

Qualitative data is generally presented in written or textual form. It involves making a written transcript and examining it for themes and meanings. Evidence for each of these is gathered from the text so that they can be discussed in turn.