9990 · 3.1.2
Common exam mistakes on 9990 Research methodology. Learn what loses marks, then practise the topic with Examiner’s Ink.
When evaluating a study in Paper 2, always link the type of experiment to its specific strengths and weaknesses. For a lab experiment, praise the high control over extraneous variables but criticise the artificiality and potential for demand characteristics.
When a question asks you to 'describe the observation' used in a core study, be precise. For example, state it was a 'covert, non-participant, structured observation' and justify each element with evidence from the study's procedure.
For Paper 2, you may be asked to suggest an alternative way to study something. If the original study used an experiment, suggesting a self-report method like a semi-structured interview is a valid alternative. Justify your choice by explaining how it would overcome a weakness of the original method (e.g., by providing insight into 'why' a behaviour occurred).
In Paper 2, if you are given a small data set, be prepared to calculate the mean, median, mode, or range. Always show your working. When asked to draw a conclusion, refer specifically to the descriptive statistics (e.g., 'The median score for Group A was 7, which is higher than the median of 4 for Group B, suggesting...').
No. A field experiment takes place in a natural setting, but the researcher still manipulates the Independent Variable (IV). In a natural experiment, the IV is an event or change that occurs naturally, without any intervention from the researcher (e.g., a change in law). The researcher simply records the effect of this change on a Dependent Variable (DV).
Yes, this is called a mixed-methods approach and is very common. For example, a questionnaire might use Likert scales to gather quantitative data on attitudes, and also include an open-ended question asking participants to explain their views, which provides rich qualitative data. This allows for both statistical analysis and a deeper, more nuanced understanding of the topic.
Reliability refers to the consistency of a measure or study. If the study were repeated, would the same results be found? Validity refers to the accuracy of a measure or study. Does it truly measure what it claims to measure? A measure can be reliable but not valid (e.g., a faulty weighing scale consistently shows you are 5kg too light), but it cannot be valid if it is not reliable (if the results are inconsistent, they cannot be accurate).