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).
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Jetzt kostenlos anmeldenData 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).
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:
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:
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:
Secondary data is defined as data that has been collected by others. It is also known as 'past data'
Examples of secondary data are:
Examples of research methods that use secondary data are:
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 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 data | Quantitative data |
Observations | Structured interviews |
Unstructured interviews | Close-ended questions in a questionnaire |
Open-ended questions in a questionnaire | Surveys (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:
Qualitative data is usually presented in written reports documenting summaries.
Some data handling examples are:
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.
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.
The data handling definition is the research process that is used to collect, record, organise and analyse data.
The steps of data handling are:
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:
What do distributions represent?
Distributions represent how the frequency of scores is distributed.
What is a normal distribution?
Normal distributions are bell-shaped and symmetrical. The mean, median and mode values are the centre of the bell, with few extreme scores.
What individual characteristics are normally distributed in the population?
For example, IQ, personality traits, shoe size, height, and weight.
What are the 3 main features of normal distributions?
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.
How are the scores distributed in normally distributed data?
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?
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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