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Edexcel GCSE Statistics > Chapter 1: Collection of Data > Flashcards

Chapter 1: Collection of Data Flashcards

1
Q

What is raw data?

A

Unprocessed data that just been collected and needs to be ordered, grouped, rounded, cleaned.

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2
Q

Define qualitative data.

A

Non-numerical, descriptive data such as eye/hair colour or gender.

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3
Q

Define quantitative data.

A

Numerical data that can be measured with numbers.

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4
Q

What is discrete data?

A

Data that only takes particular values, such as shoe size or number of people.

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5
Q

What is continuous data?

A

Data that can take any value, e.g. height, weight.

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6
Q

Define categorical data.

A

Data that can be sorted into non-overlapping categories, such as gender.

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7
Q

What is ordinal data?

A

Quantitative data that can be given an order or ranked on a rating scale.

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8
Q

What does bivariate data involve?

A

Measuring 2 variables, which can be qualitative or quantitative.

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9
Q

What is multivariate data?

A

Data made up of more than 2 variables.

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10
Q

What is the purpose of grouping data?

A

Makes it easier to spot patterns and quickly see how the data is distributed.

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11
Q

What is a primary data source?

A

Data that you have collected yourself or someone has collected on your behalf.

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12
Q

What is a secondary data source?

A

Data that has already been collected.

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13
Q

Define population in the context of data collection.

A

Everyone or everything that could be involved in the investigation.

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14
Q

What is a census?

A

A survey of the entire population.

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15
Q

What is a sample?

A

A smaller number from the population that you actually survey.

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16
Q

What is a sampling frame?

A

A list of all the members of the population from which the sample is chosen.

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17
Q

Define biased sample.

A

A sample that does not represent the population fairly.

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18
Q

What is random sampling?

A

Every item/person in the population has an equal chance of being selected.

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19
Q

What is stratified sampling?

A

The size of each strata in the sample is in proportion to the sizes of strata in the population.

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20
Q

What is systematic sampling?

A

Choosing items in the population at regular intervals.

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21
Q

Define cluster sampling.

A

The population is divided into natural groups, groups are chosen at random, and every member of those groups is sampled.

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22
Q

What is quota sampling?

A

Population is grouped by characteristics and a fixed amount is sampled from every group.

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23
Q

What is opportunity sampling?

A

Using the people/items that are available at the time.

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24
Q

Define judgement sampling.

A

When the researcher uses their own judgement to select a sample they think will represent the population.

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25
What is Petersen Capture-Recapture used for?
To estimate the size of large or moving populations where it would be impossible to count the entire population.
26
What are extraneous variables?
Variables you are not interested in but that could affect the result of your experiment.
27
What is the explanatory (independent) variable?
The variable that is changed in an experiment.
28
What is the response (dependent) variable?
The variable that is measured in an experiment.
29
What is an Explanatory (Independent) Variable?
The variable that is changed. ## Footnote This variable is manipulated to observe its effect on the dependent variable.
30
What is a Response (Dependent) Variable?
The variable that is measured. ## Footnote This variable responds to changes in the independent variable.
31
What are Extraneous Variables?
Variables you are not interested in but that could affect the result of your experiment. ## Footnote These variables can introduce noise into the results.
32
What are Laboratory Experiments?
Experiments where the researcher has full control over variables, conducted in a lab or similar environment. ## Footnote Example: measuring reaction times of people of different ages.
33
What is an example of an Explanatory Variable in a laboratory experiment measuring reaction times?
Age. ## Footnote The response variable in this case would be reaction time.
34
What are the advantages of Laboratory Experiments?
* Easy to replicate * Extraneous variables can be controlled * Results are more likely to be valid ## Footnote Controlled conditions enhance the reliability of results.
35
What are the disadvantages of Laboratory Experiments?
People may behave differently under test conditions than they would under real-life conditions. ## Footnote This could affect the validity of results.
36
What are Field Experiments?
Experiments carried out in the everyday environment where the researcher has some control over the variables. ## Footnote Example: Testing new methods of revision.
37
What is an example of an Explanatory Variable in a field experiment testing new methods of revision?
Method of revision. ## Footnote The response variable would be results in the exam.
38
What are the advantages of Field Experiments?
More accurate; reflects real-life behavior. ## Footnote These experiments tend to have higher ecological validity.
39
What are the disadvantages of Field Experiments?
* Cannot control extraneous variables * Not as easy to replicate ## Footnote This can lead to less reliable results compared to laboratory experiments.
40
What are Natural Experiments?
Experiments carried out in the everyday environment where the researcher has no or very little control over the variables. ## Footnote Example: The effect of education on level of income.
41
What is an example of an Explanatory Variable in a natural experiment studying education and income?
Level of education. ## Footnote The response variable would be income.
42
What are the advantages of Natural Experiments?
Reflects real-life behavior. ## Footnote These experiments can provide insights into phenomena that are difficult to study in controlled settings.
43
What are the disadvantages of Natural Experiments?
* Low validity * Difficult to replicate ## Footnote Extraneous variables are not controlled, which may affect results.
44
What is a Simulation in research?
A way to model random events using random numbers and previously collected data. ## Footnote Simulations help predict potential real-life outcomes.
45
What are the steps involved in conducting a simulation?
* Choose a suitable method for random numbers * Assign numbers to data * Generate random numbers * Match random numbers to outcomes ## Footnote This process can help simulate customer choices, for example.
46
What is a Questionnaire?
A set of questions used to obtain data from the population/sample. ## Footnote Can be administered via post, email, phone, or face-to-face.
47
What are Open Questions?
Questions that allow any answer but are difficult to analyze. ## Footnote They provide qualitative data but can complicate data analysis.
48
What are Closed Questions?
Questions with a fixed number of non-overlapping option boxes that allow for specific answers. ## Footnote These questions make data easier to analyze.
49
What are features of a good questionnaire?
* Easy to understand * Uses simple language * Avoids leading questions * Relevant to the investigation * Includes a time frame/unit * Non-overlapping, exhaustive option boxes * Not offensive/personal * Easy to analyze results ## Footnote These features enhance the quality and reliability of data collected.
50
What is Non-response in questionnaires?
When people in the sample do not respond to the questionnaire. ## Footnote This can be due to various factors, including lack of interest or understanding.
51
What is the Random Response Method?
Uses a random event to decide how to answer a question, ensuring anonymity. ## Footnote This method helps reduce bias in sensitive questions.
52
What is a Pilot Study?
A small-scale replica of the study to test the design and methods of the questionnaire. ## Footnote Helps identify unclear questions and estimate response rates.
53
What are Outliers in data collection?
Values that do not fit in with the pattern or trend of the data. ## Footnote Outliers can be extreme values or incorrectly recorded data.
54
What is Cleaning Data?
Fixing problems with the data by identifying and correcting/removing incorrect values or outliers. ## Footnote This process ensures the integrity of the dataset.
55
What are Control Groups?
Groups used in an experiment to ensure that the treatment is causing the experimental results. ## Footnote Control groups do not receive the treatment being tested.
56
What are Matched Pairs in experiments?
Two groups of equally matched people used to test the effect of a particular factor. ## Footnote This method helps control for unwanted variables.
57
What is a Hypothesis?
A statement that can be tested by collecting and analyzing data. ## Footnote It serves as a foundation for an investigation.
58
What are the stages of an Investigation?
* Planning * Collecting Data * Processing and Representing Data * Interpreting Results * Evaluating ## Footnote Each stage is crucial for conducting a thorough and valid investigation.
59
What is the first step in the data collection process?
Choose a hypothesis ## Footnote A hypothesis is a statement that can be tested through data collection and analysis.
60
What do you need to determine when choosing a hypothesis?
What data to collect (variables) ## Footnote Variables are the elements that can be measured or controlled in a study.
61
What is a key component of organizing data collection?
How you will record data (data collection tables) ## Footnote Data collection tables help in systematically organizing the data collected.
62
What types of data sources can be chosen for data collection?
Primary and secondary sources ## Footnote Primary sources are original data collected firsthand, while secondary sources are analyses or interpretations of primary data.
63
What are some common methods for data collection?
Questionnaire and interviews ## Footnote Questionnaires are structured forms for data collection, while interviews involve direct conversation with subjects.
64
What should be controlled during data collection?
Control factors ## Footnote Control factors are variables that are kept constant to ensure that the results are due to the variable being tested.
65
What is involved in processing and representing data?
Choosing diagrams and calculations ## Footnote Diagrams can include charts or graphs that visually represent data, while calculations provide quantitative analysis.
66
What is the purpose of interpreting results?
Drawing conclusions from the results of the diagrams and conclusions ## Footnote Interpretation involves analyzing the data and diagrams to understand their implications.
67
What is evaluated after data collection?
Evaluating methods ## Footnote This involves assessing the effectiveness of data collection methods and planning.
68
What aspects of data collection methods should be evaluated?
Strengths and weaknesses ## Footnote Understanding strengths helps reinforce effective practices, while recognizing weaknesses allows for improvement.
69
True or False: The choice of diagrams and calculations is irrelevant to data processing.
False ## Footnote Choosing appropriate diagrams and calculations is crucial for accurate data representation and analysis.

Edexcel GCSE Statistics (10 decks)

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