# Data Collection

Since you've been using StudySmarter to revise physics, you've been feeling so relaxed about GCSEs that you're sleeping great. "That was my best night's sleep in years!" you exclaim as you wake up. However, as the excellent physicist you are, you know this statement is merely a hypothesis. To use the scientific method to test your hypothesis that last night's sleep was indeed record-breaking, you'll need to collect some data. This article introduces the basic scientific principles of collecting data and why these are important to ensure reliable results.

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Contents

## Data collection meaning

The scientific method has enabled countless aspects of modern life, being responsible for the development of medicines, aeroplanes and computers. It defines the process of making a hypothesis, experimentally testing it, analysing results, and using this to iteratively refine hypotheses. The key activity needed to test the hypothesis is clearly the testing - however, to effectively analyse the results of any experiment that we run, we need to collect data from it.

The purpose of any data collection is to collect high-quality evidence that can be analysed to come up with convincing and trustworthy responses to the questions or hypotheses proposed.

Data collection is the process of gathering data on certain variables in a structured and controlled manner, allowing one to analyse the data collected to answer relevant questions and assess consequences.

### Differences between qualitative and quantitative data

Imagine you are ordering a custom-made suit or dress. When the tailor asks you how long you would like the legs to be, you have two options: you could say "long" and hope that the tailor is able to get the length about right for you, or give them a measurement, "30-inch inner leg" and know that they now have your exact size. While both these approaches could get you a great outfit, there is a key difference - the first approach uses qualitative data, while the second approach uses quantitative data.

 Type of Data Collecting methods Analysis methods Quantitative Measuring and counting are used to collect quantitative data, with data stored in numerical form. Measurements can be made manually or using sensors. Quantitative data is analysed using statistical analysis. In some applications, the data may be analysed using more advanced techniques such as machine learning and artificial intelligence. Qualitative Interviews and observation are used to gather qualitative data. Data is usually descriptive, meaning there is no set range that results are limited to. Qualitative data is studied by organizing it into meaningful groups or themes, which can then be further analysed. The data collected is non-numerical.

Here are some example comparisons of types of qualitative and quantitative data.

• The dress is pink and white in colour (qualitative).
• Students often graduate with high-scoring degrees (qualitative).
• The dresses are at least 30 inches ($$76 \,\,\mathrm{cm}$$) long and may go up to 62 inches ($$160 \,\,\mathrm{cm}$$) (quantitative).
• 75% of students graduate with a GPA between 3.3/4 and 3.7 /4 (quantitative).

The differences between qualitative and quantitative data are sometimes subtle - describing a dress as pink and white is qualitative, but counting the number of pink and white dresses in a store would be quantitative.

## Qualitative data

Returning to our example of a scientific study on the quality of sleep, you might gather some qualitative data about this by conducting interviews with people who have just woken up. You could ask questions like: "did you sleep well last night?" or "how rested do you feel?". These leave the answer open-ended, so the sleepy interviewee is free to answer however they like.

Qualitative data is information defined using descriptive language, allowing open-ended, detailed answers that are non-numerical in nature.

In some research settings, qualitative data is able to provide more in-depth and detailed information such as people's emotions or behaviours in the study of psychology. It can also help uncover new areas of research to study, as new topics may be uncovered through expansion on initial responses. This is an advantage of the open-ended nature of qualitative data capture, where data is not limited to pre-defined ranges.

### Qualitative data collection methods

• One-on-One Interviews: On a one-to-one basis, the interviewer or researcher obtains data from the interviewee, by asking them pre-prepared questions. These questions are designed to help the interviewer draw out information useful for making relevant conclusions.
• Focus groups: This type of research is done in a conversation format with a group of people. The group is generally restricted to 6-10 persons, and the debate is moderated by a moderator. The members of a group may have something in common depending on how the data is to be going to be sorted. A researcher doing a study on basketball players, for example, may select a group of people who are basketball players and a group of those who are not. They can then compare the data from the two groups.
• Record keeping: This strategy makes use of previously existing data and credible sources of information. This information can be utilized in future studies. It's like going to the library. There, one can go through books and other reference Materials to gather essential facts for the research.
• Case studies: An in-depth investigation of case studies is used to acquire data in this strategy. This method can be used to evaluate both basic and complicated issues. The strength of this strategy is how well it draws conclusions using a combination of one or more qualitative data gathering methods. A case study could yield quantitative as well as qualitative data.

## Quantitative data

In addition to collecting qualitative data about how people's self-reported quality of sleep, you may also want to record or measure some quantitative data. While this would lack the depth and detail of subjective information, a more controlled set of data gathered quantitatively allows for more straightforward numerical analysis and comparison of results. Some data you could record to quantitively measure the quality of someone's sleep could be the duration of the night's sleep, or asking participants to rate how rested they feel on a 1-10 scale.

In the case of quantitative data collection, most measured parameters have some unit assigned to them. In the examples above, the duration of sleep would be measured in minutes, while the scale would have descriptive guidelines such as 1 = no sleep, 10 = most rested possible.

A major benefit of using quantitative data is that it is objective, and not susceptible to external influences such as bias. The objective nature of quantitative data also allows us to directly compare different results. While two people may have different qualitative descriptions of what makes a good night's sleep, we can directly compare the quantifiable durations that were asleep. Finally, if data is collected in quantitative terms it is much more straightforward to statistically analyse.

### Quantitative data collection Methods

• Survey/Questionnaire: In order to obtain data from a group or a large number of people, surveys or questionnaires are often used. Both quantitative and qualitative research can be collected from surveys/questionnaires. By controlling the questions people are asked data can be gathered from humans while restricting the range of answers, which makes the subsequent processing and analysing of data simpler.
• Experiments: Many hypotheses are tested through means of an experiment. This would involve carefully designing an investigation so that the variable(s) being measured are isolated from uncontrolled factors that could influence the result, and recording objective data about the experimental results using appropriately calibrated sensors and measuring equipment.

## Types of data collection

There are two main categories of data collection - primary data and secondary data collection.

Both qualitative and quantitative data can be primary or secondary. The type of data defines if it is qualitative or quantitative, while the source of the data determines if it's primary or secondary.

### Primary data

This is data directly gathered by the person or organisation conducting the investigation, for the purpose of conducting the investigation. For example, in our sleep quality study, a source of primary data would be you running an experiment which timed the duration of people's sleep. Alternatively, qualitative data collected through one-to-one interviews would also be primary data.

### Secondary data

Secondary data is information that was not gathered for the specific investigation it is being used for, even if it is still useful. Datasets created by a different study or group can be used as a source of data for another investigation, and sometimes this is the best source of data for certain information - for example, if you wanted to find out the average height of 15-year-olds in the UK to compare to the participants in your sleep investigation, this data would be better obtained from a secondary source such as national anthropometry statistics. It's unrealistic to conduct a huge experiment for every study that may need the same dataset, so by conducting a single large-scale study other investigations can use the results as a high-quality source of secondary data.

### Derived data

Derived data is data that has been created or calculated using other data, rather than directly collected or measured. An example would be a value for average height - this figure will have been calculated using an original dataset of many individual height Measurements.

One feature of derived data is that it's hard (or impossible) to retrieve the original data from the derived data. For example, if given the average age of a population, it's impossible to determine the original set of individual lifespans data that was used to calculate the average.

## Examples of data collection

We have already discussed several examples of different types of data collection throughout this article, but here are some more to provide even more context to your understanding of data collection.

• A crisp company conducting taste trial focus groups on a new type of crisp - a focus group like this would gather qualitative data from participants about their experience of eating the crisp, which may be used to select products for further development or improvement. This data would also be primary, as the investigation is being run specifically by and for the group making the new crisp.
• The crisp company calculating an average crisp consumption per person, based on data of individual diets from a study run by the NHS. This data would be secondary, as the crisp company did not run the original study, and it would also be quantitative, as the average crisp consumption is a numerical value. This would also be derived data, as it is an average value calculated from a source dataset.
• The average weight of packets of crisps measured by the factory quality control - This data would be primary and quantitative, as it is gathered by the same group who are using it and it's numerical. The average value would again also be derived data, as the original dataset contained the weights of each individual packet.

## Data Collection - Key takeaways

• Data collection is the process of obtaining data on certain variables in a structured manner, allowing one to answer relevant questions and assess consequences.
• The purpose of any data collecting is to collect high-quality evidence that can be analysed to find convincing and trustworthy responses to the questions addressed.
• Data can be quantitative or qualitative.
• Quantitative data is defined as the value of data expressed in counts or numbers, with each data set having a distinct numerical value. Qualitative data is open-ended (not restricted to a defined range) and generally descriptive rather than numerical.
• Qualitative Data Collecting methods can include one-on-one interviews, case studies, focus groups or record keeping.
• The main method for collecting quantitative data is running an experiment, but surveys and questionnaires are also often used.
• The main types of data collection are primary and secondary data sources. Derived data can also be generated from an initial dataset.

#### Flashcards inData Collection 24

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What is data collection ?

Data collection is the process of obtaining data on certain variables in a structured manner, allowing one to answer questions relevant to a hypothesis or theory and assess consequences.

What is the importance of data collection ?

The purpose of any data collecting is to collect high-quality evidence that can be analysed to come up with convincing and trustworthy responses to the questions addressed. Data collection is an essential part of the scientific method, which bases conclusions on data-driven evidence.

What are the methods for data collection ?

Methods often used for qualitative data collection include; one-on-one interviews, focus groups, record keeping and case studies. On the other hand, surveys, questionnaires and experiments can be used to collect quantitative data.

What are examples of data collection ?

Some examples of different types of data collection include:

• Taste trials for a new snack product - this would involve running focus groups to collect primary qualitative data.
• Quality control on a snack production line - measuring the weight of each product on the line would be an example of gathering primary quantitive data. If an average weight was calculated using this data, this would be derived data.
• Finding a value for the average adult height in the UK, using national datasets. This data would be quantitative, as the value is numerical, and secondary, as the original data was collected by a different group or study.

What are types of data collection ?

There are two types of data collected: qualitative and quantitative data. These can be categorised into primary or secondary data, depending on who it was collected by.

## Test your knowledge with multiple choice flashcards

Should you take anomalies into account when drawing a line of best fit for a graph?

Does a line graph represent continuous or discrete data?

Are discrete data and continuous data both types of qualitative or quantitative data?

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