|Randomized control trial (RCT)||A study in which a number of similar people are randomly assigned to 2 (or more) groups to test a specific drug, treatment or other intervention. One group (the experimental group) has the 'intervention' being tested (e.g., nature-based ELC), the other (the comparison or control group) has an alternative intervention, a dummy intervention (placebo) or no intervention at all (i.e. usual practice such as traditional ELC). The groups are followed up to see how effective the experimental intervention was. Outcomes are measured at specific times and any difference in response between the groups is assessed statistically.|
|Randomisation||Assigning people in a research study to different groups without taking any similarities or differences between them into account. For example, it could involve using a random numbers table or a computer-generated random sequence. It means that each individual (or each group in specific types of designs) has the same chance of having each intervention. This is a very important step to reduce bias in the cause-effect relationship by distributing measured and unmeasured participant characteristics randomly between groups.|
|Controlled Before & After study (CBA)||The allocation of participants to the intervention or control group is not randomised. The key outcome is assessed among the same study population before and after receipt of the intervention. The change in outcome is compared with the same outcome measurements and changes in a suitable comparison group acting as a control group who have not received the intervention. The key outcome is assessed at the same time points in the intervention and the control group. This design may be referred to as a non-randomised controlled trial or quasi-experimental study|
|Uncontrolled Before & After Study||Similar to the CBA design but with one major difference: no control group is included to act as a comparator for those who received the 'intervention'.|
|Longitudinal study||A study of the same group of people at different times. This contrasts with a cross-sectional study, which observes a group of people at one point in time.|
|Retrospective study||A research study that focuses on the past and present. The study examines past exposure to suspected risk factors for the disease or condition. Unlike prospective studies, it does not cover events that occur after the study group is selected.|
|Cross-sectional study||A 'snapshot' observation of a group of people at one time point. Can be a study that examines the relationship between an exposure (e.g. nature-based ELC or natural elements) and outcomes of interest (e.g. health indicator) at one time point.|
|Controlled cross-sectional study||A study that examines the relationship between the exposure and outcomes of interest at one time point in two or more groups (e.g. naturalised playground and traditional playground).|
|Statistical Significance||A statistically significant result is one that is assessed as being due to a true effect rather than random chance. See P value.|
The p value is a statistical measure that indicates whether or not an effect is statistically significant. For example, if a study comparing 2 treatments (e.g. nature-based ELC vs traditional ELC) found that 1 seems to be more effective than the other, the p value is the probability of obtaining these results by chance. By convention, if the p value is below 0.05 (that is, there is less than a 5% probability that the results occurred by chance), it is considered that there probably is a real difference between treatments. If the p value is 0.001 or less (less than a 0.1% probability that the results occurred by chance), the result is seen as highly significant. However, a statistically significant difference is not necessarily practically significant. For example, nature-based ELC might increase children's levels of physical activity statistically significantly more than traditional ELC. But, if the difference in the average time spent in physical activity is 1 minute, it may not be practically significant.
If the p value shows that there is likely to be a difference between treatments, the confidence interval describes how big the difference in effect might be.
1 available from https://www.nice.org.uk/Glossary
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