Scientific Data Analysis

If you were asked to order or categorise numbers, you might order them in ascending order, or you may group even or large numbers. But numbers are not always that simple. Thus, data and scientific data analysis are not always that simple. Before researchers can go on with their scientific data analysis, they must identify what type of data they are handling.

• We will start by exploring what the data analysis scientific method definition means.
• Then we will investigate how scientific data collection and analysis are carried out in psychological research.
• Moving on, we delve into the link between statistics and analysis of scientific data, covering each level of measurement.
• From this, we will look at data analysis and interpretation, including how interviews, observations and personal records are analysed.
• Finally, we will look at some scientific data analysis examples.

Data Analysis Scientific Method Definition

The purpose of research favouring the scientific method is to either support or disprove a hypothesis. For research to do this, it should collect data and analyse empirical results and use reliable and valid methods.

Standardising procedures, meaning analysing each participant using the same protocol, ensures that the data analysis methods are reliable. The validity of the scientific data analysis can be increased by ensuring that the researcher's subjective opinion concerning the data is limited. Instead, how the data is interpreted should be based on the statistical findings of the research, i.e. it should be evidence-based.

Scientific Data Collection and Analysis

How scientific data is collected and analysed depends on multiple factors, e.g. the research method used, the type of data collected, and the type of data output - qualitative or quantitative, the researchers aim to collect.

In addition, the study's hypothesis also affects scientific data collection and analysis.

Scientific research that collectsquantitative datainitially involves identifying the level of the measurement of the data, as this affects later analysis.

However, qualitative research, like interviews, observations and diaries, has to use different analysis methods to quantitative methods, such as content orthematic analysis.

Statistics and Analysis of Scientific Data

Thelevels of measurementare also known as scales of measurement. Levels of measurement in statistics describe and classify types ofvariablesand how to measure them.

They are designed to help us understand how to interpret the data, what statistical test to use, and what information the data can give us.

There are fourlevels of measurementin psychological research, nominal, ordinal, ratio and interval data. And these can be further divided into two groups: discrete and continuous data.

Nominal and ordinal data are discrete, meaning that the data can only have a finite number of values. In contrast, continuous data, i.e. interval or ratio data, can have an infinite number of values.

Thenominallevel of measurement in psychology consists of 'named' or 'labelled data'.

Theordinallevel of measurement in psychology iscategorical数据,并有固定的值集或秩序。The order of the data is vital because it shows that one response has a lower/higher value than the other, but we cannot determine how much they quantitively differ. Ordinal data is usually collected from qualitative data.

Theratiolevel of measurement in psychology is a type of data that is classified and ranked; there is a clear difference between one point and the next. It has an absolute value of 0, meaning the numerical values cannot be less than 0.

Similar to ratio measurement,intervaldata is a type of data that can be classified and ranked, meaning there is a clear difference between one point and the next. The difference between the twolevels of measurementis that interval level data can be less than 0 (0 is not absolute).

Data Analysis and Interpretation

Case studiesuse different methods of scientific data collection called triangulation. Because of this, there are several methods that researchers must use for scientific data analysis. The most common data collection methods utilised in case studies are observations, interviews and personal records.

Observationsare usually recorded and analysed by multiple trained professionals. An example of an analysis procedure is tally counting. In this analysis, two or more professionals watch the same video and tally independently how frequently they observe a particular behaviour or pattern.

Semi-unstructured interviewsuse open-ended and closed-ended questions to obtainquantitative and qualitative data. The analysis involves taking notes from the interview transcripts, which are later categorised by themes; this process is calledthematic analysis. Data is usually reported by stating the themes and patterns identified and providing excerpts from the transcript as evidence; this form of analysis provides qualitative data.

Personal records such as diaries and letters provide qualitative information. The technique of scientific data analysis is quite different from that used forquantitative data. This is because statistical data is the simplest and most reliable method of quantitative data interpretation. Statistical analysis can be used in quantified qualitative data; this data transformation is calledcontent analysis.

Scientific Data Analysis Examples

Now let's put what we have learned into practice.

What data is collected based on the questions described in the questionnaire?