The range for the data set mentioned previously would be 5 (7-3, +1). Usually, 1 is added to the difference, to allow for the fact that scores are often rounded up or down in research. Range: The difference between the lowest and highest score in a data set. These measure how far the scores in a data set are spread out. Evaluation: Easy to work out, but it is not very sensitive, and there may be several modes in a data set.If all the scores are different then there is no mode.

If there are two modes, the data set is bi-modal. Mode: The mode is the most commonly occurring score in a set of data.

Evaluation: The median is much less affected by extreme scores than the mean, and is easy to calculate, but, is less sensitive as not all scores are taken into account.

If there are two middle scores, they are added together and divided by 2 to give the median. Median: The scores are put in numerical order, and the middle score is taken as the median.

Evaluation: Takes into account all of the data, so is the most вЂsensitiveвЂ™ measure, but, is affected by extreme values (e.g.

Mean: Calculated by adding up all of the scores, then dividing by the number of scores there are. These measure the typical score in a data set (the average). On the other hand, there may be a variance in the quality of the studies, and it may be that studies are included because they show significant results (as they are more likely to be published), so not considering all the studies which may have shown no significant result.

Meta-analyses can be useful as it reflects a (potentially) very large sample, making it easier to generalise results.

Secondary data is potentially less time-consuming and expensive, but the quality of it cannot be controlled by the researcher and it may not perfectly match the needs/aims of the study.

Although, this requires more time and effort for the researcher.

Primary data perfectly fits the study, as it has been designed for this specific purpose, and the researcher has control over it.

One example of this is a meta-analysis, which is where a researcher looks at the results of a number of studies on a particular topic in order to establish general trends and conclusions. The researcher makes use of this as part of their study, but the information was not collected for the purpose of that study. Secondary data: Data collected by someone other than the researcher (data that already exists), for example census information. Primary data: Data that has been collected by the researcher for the purposes of the study (e.g., conducting interviews, running a lab experiment). However, quantitative data is much easier to analyse and draw conclusions from, and is less open to bias and subjective opinion than qualitative data.

Qualitative data is rich, in detail, and properly reflects human experiences and behaviours, so is higher in internal validity than quantitative.

The two types of data can overlap, for example interviewing participants who have taken part in a lab study, or converting the responses to open questions into some form of qualitative data. Quantitative data: Data in the form of numbers, which is often produced from lab experiments or closed questions. Often this is produced from case studies, and unstructured interviews and observations. Qualitative data: Data in the form of words, which is rich and detailed.

Dealing with Offending Behaviour: Custodial Sentencing.

Psychological Explanations of Offending Behaviour.

The Ethological Explanation of Aggression.

Research Methods: Scientific Method & Techniques.

Explanations of Attachment: Learning Theory.

Secondary data analysis methods