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Data handling

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Data handling

Data handling concerns quantitative and qualitative data, primary and secondary data, the computation of data, and descriptive statistics. When carrying out psychological research, a core understanding of data handling is necessary for any results to be both valid and reliable. It shows how steps should be taken to collect and analyse data, and how to handle the data following collection (data sensitivity and confidentiality).

Data Handling, two people examining various forms of data displayed across behind them, StudySmarterData handling allows researchers to understand the results of their experiments, freepik.com/storyset

  • Whilst learning about data handling we will first learn about the data handling definition.
  • We will then move along to discuss the steps of data handling this will cover the different types of data handling and the data handling and analysis techniques used in psychology.
  • Throughout this explanation, you will notice data handling examples that have been added to help you with your learning!

Data Handling Definition

The data handling definition is the research process that is used to collect, record, organise and analyse data. The purpose of these steps is to collect empirical evidence to identify if the research findings support/disprove existing theories and if it rejects or accepts the null/alternative hypothesis.

There are many aspects of data handling that researchers need to keep in mind:

  • data collection
  • the type of data handling
  • ethical duties
  • how data handling and analysis will be done

Steps of Data Handling

There are several steps of data handling that researchers need to follow in a somewhat specific order. Each of the steps of data handling needs to be completed to a high standard as this can have a knock-on effect on the quality of the research and its findings.

If the stages are not done to these high standards then this can reduce the ethical standards of the research, and the reliability and validity of the results

The data handling cycle is the steps of data handling, these are:

  1. plan the research
  2. collect the data
  3. analyse the data
  4. report findings

Data collection

The first step of data collection is to plan the research that will be carried out. This includes designing the research such as the materials, type of participants, procedure and data analysis methods that the research will use.

The following step of data handling is data collection. This is the method that the researcher uses to collect data that will be later analysed.

Two main types of data are collected in psychology research; primary and secondary data. The type of data collected is determined by the type of research method that the researcher uses in the study.

Primary data is defined as data the researcher collects. Some call it 'real-time data'

Examples of research methods that collect primary data are:

  • Questionnaires
  • Observations
  • Interviews

Secondary data is defined as data that has been collected by others. It is also known as 'past data'

Examples of secondary data are:

  • Previously published results
  • Diaries/autobiographies/memoirs
  • Letters/newspapers
  • Doctors notes/ medical reports
  • Government published statistics etc.

Examples of research methods that use secondary data are:

  • Meta-analyses
  • Systematic reviews

Some research can use primary and secondary data for analysis such as case studies.

An important thing for researchers to keep in mind during the data collection stage is that the data needs to be stored securely. The participant's personal details should not be revealed to anyone. The purpose of this is to ensure that the ethical responsibilities that researchers have towards participants are not tarnished.

The Types of Data Handling

The next step of data handling is to analyse data. The type of data handling affects how it is analysed later. There are two types of data handling; qualitative and quantitative data.

Qualitative data is non-numerical data, aka descriptive data

Quantitative data is numerical data, aka quantities of information

The type of data obtained depends on the research method used. Some examples of research methods that collect qualitative or quantitative data are listed below.

Qualitative dataQuantitative data
ObservationsStructured interviews
Unstructured interviewsClose-ended questions in a questionnaire
Open-ended questions in a questionnaireSurveys (close-ended)

Qualitative data is usually analysed using procedures called thematic analysis or content analysis.

Thematic analysis is a qualitative analysis type of data handling. It is used by identifying themes mentioned throughout the qualitative text. A report is then written up that identifies the themes and gives extracts from the data as evidence.

Content analysis is an analysis type of data handling that changes data from qualitative to quantitative. It is done by identifying themes in the content and tallying how frequent each one is observed. Later, statistical analysis can be done on this.

Quantitative data is usually presented in tables, graphs and statistics. Illustrative quantitative data handling examples used in psychology research include:

  • frequency tables
  • bar charts
  • histograms
  • scatter diagrams

Qualitative data is usually presented in written reports documenting summaries.

Data Handling Examples

Some data handling examples are:

  • Normal distributions: This is a data handling example that involves measuring how the values of the data fall around the mean score. Understanding if data is normally distributed is crucial because it influences what statistical tests can be later used. Ideally, researchers want to collect normally distributed data.

Data that is non-normal can only use non-parametric statistical tests to test the hypothesis. These tests are less restrictive and so it is easier for the results to be more inaccurate than parametric tests that can only be used on normally distributed data.

  • Descriptive statistics: Descriptive statistics are statistics that describe the variables that are being investigated in the research. Types of descriptive statistics that are commonly measured in psychology research are:
    • Mean: this is the most common descriptive statistic that is measured. This is calculated by adding all the values together and dividing it by the number of data points there are in the analysis.
    • Mode: this is the most common number that is identified in the data
    • Median: this is measured by putting the data in numerical order and identifying the middle-value
    • Range: this is a measure of dispersion which means that it measures the spread of the data. This can be identified by misusing the smallest number from the largest
  • Computation: Researchers often express data in various forms such as standard form, decimal form, fractions and percentages. On some occasions, researchers may convert one numerical form to another. During working out and reporting computations, researchers need to follow psychological guidelines, for instance, significant figures need to be reported to two significant figures.

Research that is looking for apparent differences between scores of two experimental groups may convert the standard form scores into percentages. Percentages allow for researchers to see clear differences between the scores of the two groups.

The final step of data handling is to report the results using data analysis. In this stage, researchers write a scientific report that describes the details of the research. When reporting the results, researchers need to state whether the findings support/ disprove the hypotheses proposed at the start of the experiment.


Data handling - Key Takeaways

  • Data handling is an important step in psychology research. It is the steps that researchers take to store and dispose of the data collected in their research.
  • The data handling definition is the research process that is used to collect, record, organise and analyse data. The purpose of these steps is to collect empirical evidence to identify if the research findings support/ disprove existing theories and the hypothesis that the research formulated.
  • There are several steps of data handling that researchers need to follow:
    1. Planning the research
    2. Data collection; researchers can collect primary or secondary data
    3. Analyse data: the type of data handling affects how it is analysed later, and there are two types of data handling; qualitative and quantitative data
    4. the final stage is to report the results
  • Some analysis data handling examples are: normal distribution, descriptive statistics, and computation

Frequently Asked Questions about Data handling

The data handling definition is the research process that is used to collect, record, organise and analyse data. 

The steps of data handling are:

  1. planning the research 
  2. data collection 
    • during this stage, the researcher needs to ensure that the data collected is ethically stored 
  3. data analysis 
    • the analysis involves several stages such as measuring descriptive statistics, normal distribution, inferential statistics and doing computations.
  4. reporting the results found 

There are several types of data handling that are determined by factors, such as the type of data used (qualitative or quantitative). These determine what analysis method the researcher may use, such as content/thematic analysis or descriptive statistics. 

Data handling is important because the purpose of these steps is to collect data (empirical evidence) to identify if the research findings accept or reject the alternative hypothesis/null hypothesis, the hypothesis that the research formulated.

The data handling cycle is the steps of data handling, these are:

  • plan the research 
  • collect the data 
  • analyse the data  
  • report findings 

Final Data handling Quiz

Question

What do distributions represent?

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Answer

Distributions represent how the frequency of scores is distributed.  

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What is a normal distribution?

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Answer

Normal distributions are bell-shaped and symmetrical. The mean, median and mode values are the centre of the bell, with few extreme scores.  

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What individual characteristics are normally distributed in the population?

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For example, IQ, personality traits, shoe size, height, and weight.

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What are the 3 main features of normal distributions?

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1. They are symmetrical, meaning that the distribution of scores larger than the mean should be symmetrical to the distribution of scores smaller than the mean.

2. Normal distribution is because it is bell-shaped. The mean, median and mode values tend to be similar or the same and create the centre of the distribution. 

3. The tails of the distribution meet the x-axis at infinity.

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Question

How are the scores distributed in normally distributed data? 

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Answer

  • Most of the data is clustered around the mean value that is in the centre of the distribution. 
  • The frequency of scores at the tails of the distribution is much lower than in the centre. 
  • Scores are distributed symmetrically around the mean.

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Question

Let's say that anxiety is a normally distributed trait in the population. What's the probability of having average levels of anxiety compared to extreme levels of anxiety?

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Answer

The probability of scoring around the average is larger than getting extreme scores in the case of normally distributed traits. This is because normal distribution suggests that anxiety levels for most people in the population will be clustered around the mean.

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Question

Which value measures the average score?

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Answer

mean

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Which value reflects the middle score?

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Answer

median

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Which value reflects the most frequent score?

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Answer

mode

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Question

Is the following statement true or false?

When the data is normally distributed mean, median and mode tend to be the same or very similar. 

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Answer

True

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Question

Which distributions are symmetrical?

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Answer

Normal distributions

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Question

Describe the characteristics of skewed distributions.

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Answer

Skewed distributions are not symmetrical, they have their peak off centre and have an extended tail in one direction. The mean is no longer in the centre it is shifted due to a large number of extreme scores, to one side of the distribution. Such distributions no longer form a bell curve. 

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Question

What are positively skewed distributions?

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Answer

Positively skewed distributions have their peak shifted to the left with a longer tail to the right.

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What are negatively skewed distributions?

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Negatively skewed distributions have their peak shifted to the right with a longer tail to the left.

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What are the applications of normal distributions in psychology?

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Answer

Normal distributions can be helpful for clinical diagnosis


In research normally distributed data is also necessary to test the statistical significance of our results using parametric statistical tests like the t-test.

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Question

What are the steps of data handling? 

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Answer

The steps of data handling are:

  1. plan the research 
  2. collect the data 
  3. analyse the data 
  4. report the results 

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Question

What are the aspects of data handling that researchers need to keep in mind? 

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Answer

 There are many aspects of data handling that researchers need to keep in mind. Some of these are:

  • data collection 
  • the type of data handling 
  • ethical duties 
  • how data handling and analysis will be done 

Show question

Question

What is the data handling definition? 

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Answer

The data handling definition is the research process that is used to collect, record, organise and analyse data.

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Question

What is the purpose of data handling? 

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Answer

The purpose of these steps is to collect empirical evidence to identify if the research findings support/ disprove existing theories and the hypothesis that the research formulated. 

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Question

How can researchers ensure that they do not breach their ethical duties during the data handing steps? 

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Answer

Researchers can ensure that they do not breach their ethical duties is by: 

  • storing data in a secure place 
  • ensuring that participants confidentiality is maintained 

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Question

Which type of data is analysed using content or thematic analysis? 

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Answer

Qualitative data 

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Question

Which type of data is analysed using computations, normal distribution and descriptive statistics? 

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Answer

Quantitative data 

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Question

What type of data can be collected during the data collection?

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Answer

Researchers can collect primary, secondary or both types when collecting data for research.

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Question

Describe what happens in the last step of data handling. 

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Answer

The final step of data handling is to report the results. In this stage, researchers write a scientific report that describes the details of the research. When reporting the results, researchers need to state whether the findings support/ disprove the hypotheses proposed at the start of the experiment. 

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Question

What is the name of the model that describes the steps of data handling? 

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Answer

The model that describes the steps of data handling is called the data handling cycle. 

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Question

Which of the following research methods collect qualitative data? 

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Answer

Unstructured interviews 

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Which of the following research methods collect quantitative data? 


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Answer

Structured interviews 

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What is the definition of primary data? 

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Answer

Primary data is defined as data that the researcher collects themself. 

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What is the definition of secondary data? 

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Secondary data is defined as data that the researcher has not collected themself. 

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What is the difference between primary and secondary data? 

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Answer

Primary and secondary are both types of data. However, they differ in many key ways, such as:

  • how the data is collected 
  • how is the data analysed 
  • the merits and demerits of both types of data. 

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Which type of data would be used when investigating historical events? 

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Answer

Secondary data 

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Which of the following methods are used to collect primary data? 

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Answer

Interviews 

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What are the advantages of using primary data? 

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Answer

The advantages of primary data are:

  • the researcher can collect all of the information that they need to investigate the research question, aims and hypothesis
  • as the researcher collected the data themself it is easier to identify/ test the reliability and validity of the data collected
  • the researcher will collect up-to-date information. Over time, the results from research may change due to different factors such as advancements in society
    • therefore, this type of data may be considered more useful. 

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What are the advantages of collecting secondary data? 

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The advantages of collecting secondary data are:

  • it is usually not a time-consuming method to collect data
  • allows researchers to investigate concepts that cannot be tested now 
  • meta-analysis/ systematic reviews rely on previously published reviews. These types of research are useful because they use empirical evidence to summarise the key findings of existing research regarding a phenomenon.  

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What are the disadvantages of collecting primary data? 

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Answer

The disadvantages of collecting primary data are:

  • it can be costly
  • it can be time-consuming 
  • it usually requires more effort than secondary data

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What are the disadvantages of collecting secondary data? 

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Answer

The disadvantages of collecting secondary data are:

  • there can be ethical issues surrounding certain secondary data such as medical/ psychiatric notes. Such as confidentiality and causing participants distress
  • it can be difficult for the researchers to establish the reliability and validity of the data
  • data that the researcher may be interested in may be missing - this reduces the utility of the research.

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Does secondary data always rely on collecting information from participants?

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Answer

No

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What type of data is used when researchers carry out meta-analyses or systematic reviews? 

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Answer

Secondary data

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Can researchers collect primary and secondary data in conjunction when conducting a research project? 

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Answer

Yes 

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What is the quantitative data definition? 

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Answer

The quantitative data definition is essentially numerical data that is used in research to summarise, describe, evaluate and analyse datasets. 

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What are some of the uses of quantitative data in psychology research? 

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Answer

Some of the uses of quantitative data in psychology research are:

  • researchers can easily identify patterns and trends in data 
  • researchers can use inferential statistics to identify if results are significant (not due to chance) and identify if the data supports/ rejects the hypothesis proposed

  • can easily compare results from other research 

  • can use quantitative data to describe the data that was collected.

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Which of the following is not an example of data that does not collect quantitative data? 

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Answer

Open-ended questions 

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Which of the following describes quantitative data? 

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Answer

Numerical data 

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What is the purpose of using frequency tables in research? 

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Answer

To describe the characteristics of data 

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What is used to illustrate frequency tables? 

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Answer

Bar charts 

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Question

What is used to illustrate correlational data? 


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Answer

Scatterplots

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Question

Which of the following graphs would be used when a researcher wants to illustrate the frequency table output that collected data from a large sample? 

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Answer

Histogram 

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Question

When including charts/ graphs in psychology reports, how does the researcher need to format this?  

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Answer

When including charts/ graphs in psychology reports, the researcher needs to:

  • title the chart/ graph 
  • label the axis
  • interpret the chart/ graph

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Question

What can be information can be identified from a scatterplot? 

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Answer

How a scatter plot looks can be used to:

  • identify the relationship between two variables (positive, negative or, none) 
  • identify the strength of the relationship (strong, weak or, moderate)


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Question

What are the advantages of quantitative data? 

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Answer

Some of the advantages of quantitative data are:

  • Visualising data may make it easier for the reader to understand the data 
  • Quantitative data allows researchers to identify patterns/ trends and analysis of data 
    • from this, the researcher can accept/ reject the hypothesis 
  • It is considered an objective and scientific way to assess hypotheses

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