Summary research in biomedical sciences
Lecture 1 – Recap IBMS
Variables= observable or hypothetical events that can change and whose changes can be
measured in some way
• Independent variables - Explanatory variables
• Dependent variables - Response variables
• Extraneous variables
• Confounding variables
Extraneous variable= variables that are not of interest to the researcher but that might
influence the variables of interest if not controlled
= variables that provide an alternative explanation
If controlled (i.e. kept constant or manipulated):
If not controlled: extraneous variable = confounding variable
A controlled experiment→ keeping the extraneous variables constant
,Variables: Levels of measurement
Research design
• Research question
• Causal effect or association?
• Dependent variable(s)
• Measurement
• Type (nominal, ordinal, discrete, continuous)
• How many?
• Independent variable(s)
• Measurement
• Type (nominal, ordinal, discrete, continuous)
• How many?
• Manipulation
• Compare groups or conditions? How many?
• Are measurements/manipulations dependent/within-subjects/paired or
independent/between-subjects/not paired?
,
, Frequency: how often each value in the data set occurs
Proportion: how often each value in the data set occurs in proportion to other values
Median = the value at the median location
= the value at the middle of the sample if scores ranked from lowest to highest
• If odd number of data: median = value at median location: e.g. 1, 2, 5, 10, 12 –median = 5
• If even number of data: median = mean of the adjacent values of the median location
Mode = the most frequently occurring value in a data set
2 modes: data are BI-MODAL
Mean
- Use with quantitative data
- Takes account of the exact distances between values in the data set
- Powerful statistic used in estimating population parameters and in inferential
statistics
- Sensitive to outliers (extreme values in the data set)
Median
- Use with ordinal data
- Takes account only of the position of ranked values in the data set
- Unaffected by outliers (extreme values in the data set)
Mode
- Typically used with nominal data
- Does not take account of the exact distances between values in the data set, nor the
rank order
- Unaffected by outliers
- Uninformative in small data sets
Lecture 1 – Recap IBMS
Variables= observable or hypothetical events that can change and whose changes can be
measured in some way
• Independent variables - Explanatory variables
• Dependent variables - Response variables
• Extraneous variables
• Confounding variables
Extraneous variable= variables that are not of interest to the researcher but that might
influence the variables of interest if not controlled
= variables that provide an alternative explanation
If controlled (i.e. kept constant or manipulated):
If not controlled: extraneous variable = confounding variable
A controlled experiment→ keeping the extraneous variables constant
,Variables: Levels of measurement
Research design
• Research question
• Causal effect or association?
• Dependent variable(s)
• Measurement
• Type (nominal, ordinal, discrete, continuous)
• How many?
• Independent variable(s)
• Measurement
• Type (nominal, ordinal, discrete, continuous)
• How many?
• Manipulation
• Compare groups or conditions? How many?
• Are measurements/manipulations dependent/within-subjects/paired or
independent/between-subjects/not paired?
,
, Frequency: how often each value in the data set occurs
Proportion: how often each value in the data set occurs in proportion to other values
Median = the value at the median location
= the value at the middle of the sample if scores ranked from lowest to highest
• If odd number of data: median = value at median location: e.g. 1, 2, 5, 10, 12 –median = 5
• If even number of data: median = mean of the adjacent values of the median location
Mode = the most frequently occurring value in a data set
2 modes: data are BI-MODAL
Mean
- Use with quantitative data
- Takes account of the exact distances between values in the data set
- Powerful statistic used in estimating population parameters and in inferential
statistics
- Sensitive to outliers (extreme values in the data set)
Median
- Use with ordinal data
- Takes account only of the position of ranked values in the data set
- Unaffected by outliers (extreme values in the data set)
Mode
- Typically used with nominal data
- Does not take account of the exact distances between values in the data set, nor the
rank order
- Unaffected by outliers
- Uninformative in small data sets
