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| Questão | Responda |
| Data analysis and presentation including; measures of central tendency, measures of dispersion, content anaylsis | Data analysis -graphs are a useful way of summarising data in a meaningful way, which enable psychologists to easily see trends or patterns in data. -4 graphs are commonly used to display quantitative data 1. Histogram 2. bar chart 3. scattergram 4. frequency polygon |
| Terminology | Nominal data- are data placed in categories e.g.the no. of people with blue eyes, brown eyes, etc. -each category is mutually exclusive, which means that Pp can only appear in 1 category. -ordinal data- are data that can be put into rank order, e.g. exam marks, ratings of aggression, birth order in a family etc. -interval data- are data that are measured in terms of equal intervals e.g. minutes, centimetres, degrees, items in a memory test etc. |
| 1. Histogram | -a histogram consists of a series of vertical bars of equal width. -the bars are continuous so there is no space between them. -it is used for ordinal and interval data. -the units of measurement are shown on the x-axis -single values can be used on the x-axis, or data can be grouped. -frequency is represented by the area of each bar. -in cases where the class widths are all equal, you can use the value that they go up to on the y-axis as equivalent to their area. -they can be used to show data grouped together into class intervals. e.g. Pps are asked to do a cognitive task as quickly as possible and they are timed. -each score is different so it would not be useful to plot the individual scores on a histogram. -in this case you would group the data as so: 0-59secs, 1min-1min59, 2-2 mins 59 ect |
| 2. Bar charts | -a bar chart is used for nominal data -it consists of a set of vertical bars with a space between each of them. -each bar represents a different category (non-continuous data) -the bars can be placed in any order along the x-axis. -the categories are shown on the x-axis -the frequency of each category is shown on the y-axis. |
| 3. scattergram (scatter graph) | -this is used for showing the relationship between 2 variables, that is for showing correlations. -data from 1 variable are shown on the y-axis and data from the other variable are shown on the x-axis. -a scattergram shows whether there is a trend towards a positive or negative correlation. -the closer the points on the graph are to a straight line, the stronger the correlation. |
| 4. Frequency polygon | -this can be used as an alternative to the histogram -this is particularly useful when you need to show 2 sets of data on the same graph. |
| Measures of central tendency including median, mean, mode | -measures of central tendency tell the researcher where the average is in a set of data. -they are averages, that is single values that are calculated to represent a set of numbers by providing the most typical (central value). -3 main measure of central tendency are: 1. mean 2. median 3. mode |
| 1. Mean | -this is known as the statistical average. -the arithmetic average -calculated by adding all the scores together in a set of data and then divide that total by the number of scores. |
| 2. Median | -this is the middle value of a data set. -calculated by ranking all of the data scores in order and finding the middle value. -if there is a even number of scores, you should add the 2 middle scores together and divide by 2. |
| 3. Mode | -this is the mOst frequent occurring value. -it is simply calculated by putting the data in order and work out which score occurs the most. |
| 1. Mean ADVANTAGES | -involves all the numbers -all of the results are taken into consideration -most sensitive measure, taking all the scores into account. |
| 1. Mean DISADVANTAGES | - can be significantly affected and distorted by a single extreme value in a set if included in the calculation. -unrepresentative -it can only be used for data that are at least interval. |
| 2. Median ADVANTAGES | - less likely to be affected by the extreme scores -easier to work out than the mean -can be used on ordinal or interval data. |
| 2. median DISADVANTAGES | - doesn't involve all of the numbers/scores -ignores most scores -not very sensitive or representative of the data if scores are generally clustered at high and low levels or if there are only a few values in a set. -not much use for small data sets |
| 3. Mode ADVANTAGES | - unaffected by extreme scores -easy to work out -only measure of central tendency for nominal data |
| 3. Mode DISADVANTAGES | -doesn't use all of the scores -tells us nothing about other scores in the set and may not be typical (central) -limited usefulness if there is more than one modal score in a set. -not useful for small sets of scores -unrepresentative/inaccurate. |
| Measures of dispersion, including ranges | -these show how the scores in a set are spread out. -this is important because it tells us whether the scores are similar to one another or whether they vary hugely. -the 2 measures of dispersion are 1. range 2. standard deviation |
| 1. Range | -the difference between the highest and lowest score in the set of data. -this should be used when you wish to make a basic measure of the variation within the data and the data is consistent. -if there are extreme scores the range is inappropriate as it will be a distorted measure of variation. ADVANTAGE- quick and easy to calculate DISADVANTAGE- take account of all the scores. |
| The interquartile range | -when there are a few extreme scores, the interquartile range might be used. -it is the range of the middle 50 per cent scores. - it is calculated from the mid-points between the upper and lower scores in big numbers and numbers next to each of them STRENGTH- the variation ratio is quick to calculate and easy to understand WEAKNESS- in small data samples, there may not be a mode so the variation ratio cannot be calculated. |
| 2. Standard deviation | -this is the measure of the spread of scores around the mean -i.e. it tells us how far the scores are scattered around the mean. -it is the most powerful measure of dispersion as it takes all scores into account (unlike the range) -as a result, it is often used by researchers who want to know more about dispersion of their data. LARGE Standard deviation- tells us that there was much variation around the mean. SMALL Standard deviation- tells us that the data was closely clustered around the mean ZERO Standard deviation- tells us that all the data values were the same. |
| More on Standard deviation | -it should be used when you wish to make a very sensitive measure of dispersion ADVANTAGES- takes into account all of the scores -it is a sensitive measure of dispersion -could be easily distorted by extreme values DISADVANTAGES- more difficult to calculate compared to the range. -should only be calculated on data measured on an interval scale. |
| Analysis and interpretation of correlational data | -correlations are designed to investigate the STRENGTH and DIRECTION of a relationship between 2 variables. -the strength of the correlation is expressed by the CORRELATION COEFFICIENT. -the correlation coefficient is always between +1 and -1 where; +1 represents a perfect positive correlation -1 represents a perfect negative correlation - a correlation coefficient of 0 means that there is no correlation between the 2 variables. |
| Positive correlation | as values on 1 variable increase, so do values on the other variable |
| Negative correlation | as values on one variable increase, values on the other variable decrease. |
| Correlation coefficient | -this is a statistic that measures the strength of the relationship (correlation) between 2 variables. -the scale of measurement ranges from +1 to -1. -the numerical value indicate the strength of the relationship. |
| Presentation of Qualitative data | Terminology 1. Quantitative data- comes in numerical form, e.g. distances expressed in metres, scores on a memory test, yes/no answers on a questionnaire. 2. Qualitative data- cannot be expressed in numbers and are obtained from, e.g. interview or observations. 3. content analysis- is a systematic research technique for analysing non-numerical data, e.g. material contained in interviews, documents, children's comics, TV programmes, newspapers, adverts, etc. making it quantitative |
| Qualitative data | -collected from e.g. an interview can be very rich and detailed. -they can take the form of what was actually said in the interview (transcript of actual words); observations of body language, facial expressions, etc. during the interview; self-report from the interviewees about their feelings during the interview etc. -such material can provide valuable insights for psychologists, but; 1. it is very difficult to analyse the data and make sense of them for other people to understand 2. it is open to bias where the researcher makes selections and interpretations that fit their own theoretical standpoint or that are particularly relevant to their own research. -in order to make the data more accessible, some researchers choose to use the method of content analysis. -Qualitative data are very often used when researchers report a case study in which a single individual is investigated in great detail. -the data are often presented in the form of direct quotations from the Pp. |
| Processes involved in content analysis | 1. decide what material to sample. e.g. if the area of interest is personal adverts, the researcher would probably want to sample a range of different publications (newspapers or journals that reflect different political learnings, target readership, price, locality). -if the researcher is interested in looking at the content on TV ads, they might want to have a narrow focus (e.g. just those ads that are on during children's programmes) or to have a wider focus, looking at different commercial channels at various times of the day/night. 2. decide what type of themes and categories of response might emerge from these materials. -in the traditional model of content analysis, themes are decided before the material is looked at. -this can be done by using a pilot study or simply through the familiarity of the researcher with the type of materials being investigated. -however, some researchers only decide on the themes after the sample materials have been gathered 3. create a coding system based on the predetermined themes. 4. the researcher usually collects a large no. of examples of their chosen sample materials. 5. coders are given the sample materials to read and asked to categorise items found in the materials according to the coding units. 6. examples of coding units are: -words- analyse for status-related words in personal ads -themes- analyse for e.g's of helping behaviour in children's comics. -character- analyse for gender stereotypical behaviour in TV ads. |
| Content analysis cont.... | -it is an effective way of presenting qualitative data in a way that is easy to understand. -it can have high validity because it is usually gathered in natural settings. -however, the interpretation of the data can be subjective. -in identifying the coding units, researchers can be inconsistent or impose their own meaning systems on the data. -for this reason, the technique is often unreliable. -1 way of trying to improve reliability is to have multiple coders who negotiate the coding units together. -correlational techniques are often used to check for the reliability of coders. |
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