Data Analysis and Handling

Quantitative Data

Numerical data that can be measured and written down with numbers. Often collected through experiments or closed questionnaires.

Qualitative Data

Non-numerical data that describes qualities or characteristics. Often collected through interviews, open-ended questionnaires, or observations.

Primary Data

Data collected first-hand by the researcher specifically for the purpose of the investigation.

Secondary Data

Data that has been collected previously by someone else for a different purpose.

Meta-analysis

A statistical technique that combines the findings of multiple studies to draw a general conclusion.

Mean

The arithmetic average. Calculated by adding all values and dividing by the number of values.

Median

The middle score in a dataset when values are ordered from lowest to highest.

Mode

The most frequently occurring score in a dataset.

How to calculate the Mean

Add all the scores together and divide by the total number of scores.

How to calculate the Median

Arrange the numbers in order and find the middle one. If even number of values, average the two middle values.

How to calculate the Mode

Identify which value appears most frequently in the dataset.

Range

A measure of dispersion calculated as the difference between the highest and lowest values.

How to calculate the Range

Subtract the smallest value from the largest value.

Standard Deviation

A measure of how spread out values are around the mean. The higher the SD, the more variability in the data.

How to calculate Standard Deviation

Find the mean. Subtract the mean from each value and square the result. Find the average of these squared differences. Take the square root of that average.

Calculation of Percentages

Part ÷ Whole × 100

Positive Correlation

As one variable increases, the other variable also increases.

Negative Correlation

As one variable increases, the other decreases.

Zero Correlation

No relationship between the two variables.

Descriptive Statistics

Summary measures that quantitatively describe features of a dataset, such as central tendency and dispersion.

Measures of Central Tendency

Mean, median, and mode – these describe the center of a data set.

Measures of Dispersion

Range and standard deviation – these describe how spread out the values are.

Tables

Used to present raw or summary numerical data in rows and columns.

Graphs

Visual representations of data. Includes line graphs, bar charts, histograms, and scattergrams.

Scattergrams

Graphs used to show correlation between two variables using points on a grid.

Bar Charts

Used to display categorical data with rectangular bars showing frequencies.

Histograms

Used for continuous data grouped into ranges. Bars touch each other.

Correlation Coefficient

A statistical value (between -1 and +1) showing the strength and direction of a correlation.

Analysis of Correlation

Evaluating whether variables are related and how strongly they move together.

What does it mean if the correlation is closer to +1

strong positive

What does it mean if the correlation is closer to -1

strong negative

What does it mean if the correllation is closer to 0

No correlation

What is a causation?

Means one variable directly causes a change in another.

What is a correlation?

A statistical relationship between two variables.

What are the key differences between causation and correlation?

Causation - is always a cause and effect, also directional, is an experimental control

Correlation - is always to do with association, can be positive or negative, observational study