# Understanding Types of Data and Sampling Methods

Understanding Types of Data and Sampling Methods
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Slide 1: Slide

This lesson contains 15 slides, with interactive quizzes and text slides.

## Items in this lesson

Understanding Types of Data and Sampling Methods

#### Slide 1 -Slide

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Learning Objectives
At the end of the lesson, you will be able to differentiate between primary and secondary data.

#### Slide 2 -Slide

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#### Slide 3 -Mind map

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Primary Data vs. Secondary Data
Primary data: Your own or group data that is collected firsthand on the day of the fieldwork.

#### Slide 4 -Slide

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Quantitative Data vs. Qualitative Data
Quantitative data: Data with a numerical value.
Qualitative data: Data as words, views, or feelings.

#### Slide 5 -Slide

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Reliability in Data Collection
Reliability: Where the data can be trusted to give an accurate representation.

#### Slide 6 -Slide

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Bias in Sampling
Bias: Where your sampling does not represent the whole data set because you have selected preferred data.

#### Slide 7 -Slide

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Sampling Techniques
Sampling: A selection of data to represent the information you want to collect, sampled in a planned way to avoid bias.

#### Slide 8 -Slide

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Systematic Sampling
Systematic sampling: Having a system to collect samples, such as every 2m along a transect or every 5th person.

#### Slide 9 -Slide

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Random Sampling
Random sampling: Using random numbers to select a sample, ensuring an unbiased selection.

#### Slide 10 -Slide

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Stratified Sampling
Stratified sampling: Using prior knowledge to decide where you collect samples from a selected area or group.

#### Slide 11 -Slide

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Definition List
Primary data: Your own or group data that is collected firsthand on the day of the fieldwork.
Secondary data: Data collected from another source, which may be current or historical, and collected by someone else.
Quantitative data: Data with a numerical value.
Qualitative data: Data as words, views, or feelings.
Reliability: Where the data can be trusted to give an accurate representation.
Bias: Where your sampling does not represent the whole data set because you have selected preferred data.
Sampling: A selection of data to represent the information you want to collect, sampled in a planned way to avoid bias.
Systematic sampling: Having a system to collect samples, such as every 2m along a transect or every 5th person.
Random sampling: Using random numbers to select a sample, ensuring an unbiased selection.
Stratified sampling: Using prior knowledge to decide where you collect samples from a selected area or group.

#### Slide 12 -Slide

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Write down 3 things you learned in this lesson.

#### Slide 13 -Open question

Have students enter three things they learned in this lesson. With this they can indicate their own learning efficiency of this lesson.
Write down 2 things you want to know more about.

#### Slide 14 -Open question

Here, students enter two things they would like to know more about. This not only increases involvement, but also gives them more ownership.