Selecting a test: lesson 2 Choice 3 Levels of data

1st Choice Difference or Correlation The first decision you have to make is what the hypothesis is testing. Is it talking about a difference between two conditions or is it asking about the relationship between two variables. It’s all in the language used.

Revisiting the past Recall the following experiments that you have studied, how are the participants measured. Primacy/Recency Milgram’s obedience study Asch’s conformity study

Different types of data For the purposes of statistical analysis, there are different types, or levels, of data. On this basis, different inferential tests work on different levels of data.

Data can be collected on 3 levels. Nominal Ordinal Interval If a researcher has categorised a participant in some way then it is nominal data. However, if each participant has some kind of score then it is interval or ordinal.

Have a look at these 2 data tables. The Distribution of respondents answers to the statement ‘ More criminals should be treated through rehabilitation rather than imprisonment’. Participants’ raw scores in a test of memory Participant Score 1 11 2 9 3 4 15 5 7 6 Strongly agree 12 Agree 27 Disagree 33 Strongly Disagree 15

Categorising Data In small groups, devise a categorising system to divide the class up. Think about what will define each group and how this will be measured. Feedback

Nominal Data Nominal data are in categories. If a person was categorised as tall or short, this would give us nominal data on them. Other examples of nominal data include classifying people as introvert or extrovert; identifying people as high, moderate or low achievers; observing whether people did or did not stop at traffic lights.

Essay grades can be nominal data Essay grades can be nominal data. By deciding whether a student has passed or failed an essay, teachers are putting their performance into a category. This also illustrates why nominal data is the lowest level of data-because it gives us limited information on the variable being measured. In the case of an essay, the variable is a student’s understanding of a topic. What indication does pass or fail give?

Which one of these experiments, if any, is nominal level data…….why? Primacy/Recency Milgram’s obedience study Asch’s conformity study

Ordinal Level This is a measurement scale in which the scores are ranked.    For example the order of the finishers of a race – first, second, third and so on. 

Importantly no information is provided about the size of the difference between any two scores.  For example if we know the order of the finishers of a race this does not tell us anything about the time difference between the racers. Produce some ordinal data to describe the class.

Interval Ratio Level This is a measurement scale with equal units of intervals throughout the scale.   For example, the times in seconds the racers finished the race.    If the measurement scale does not have an absolute zero we call this interval data.  When the lowest point on a scale is zero and the units of intervals are equal we call this interval ratio data.

If the measurement scale does not have an absolute zero we call this interval data.  When the lowest point on a scale is zero and the units of intervals are equal we call this interval ratio data. The distinction does not matter when we are choosing a test. Produce some examples of interval level data.

Examples Researchers want to find the difference between male and female estimates of stopping distances. On a questionnaire about social behaviour there is a question asking: On a scale of 1-10 how aggressive is your child? 1 is not at all 5 average and 10 very aggressive Participants have to choose ‘snog’, ‘marry’ or ‘avoid’ when shown a set of photographs of possible partners

Answers Interval Ordinal Nominal

The last choice: Related or Unrelated Related samples (e.g. repeated measures design or matched pairs design). Unrelated samples (e.g. independent measures design).  This is very simple knowledge from AS.

Try The Card Sort Test your knowledge of the experimental designs. Match the strengths and weaknesses to the designs.