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.
The data analysis scientific method definition is a standardised process that accurately and objectively analyses data from research observed in the study (i.e. empirical).
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.
A hypothetical study hypothesised that there is a link between rain and umbrella sales. In this study, a correlational analysis would likely be employed.
Scientific research that collects quantitative data initially 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 or thematic analysis.
Statistics and Analysis of Scientific Data
The levels of measurement are also known as scales of measurement. Levels of measurement in statistics describe and classify types of variables and 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 four levels of measurement in 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.
The nominal level of measurement in psychology consists of 'named' or 'labelled data'.
An example of a nominal level measurement question is What is your gender? So the answers male, female and other are forms of nominal data.
The ordinal level of measurement in psychology is categorical data, and the values have a fixed set or order. 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.
An example of a question with ordinal measurement is What is your socioeconomic class? So the ordinal data could be working class, middle class, and upper-class.
The ratio level 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.
Participants' height, age, and travel speed are data that use a ratio measure. Your height cannot be negative, your age cannot be less than 0, and you cannot be travelling at a minus speed.
Similar to ratio measurement, interval data 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 two levels of measurement is that interval level data can be less than 0 (0 is not absolute).
An example of interval data is the temperature which can be recorded at 0 and below.
Data Analysis and Interpretation
Case studies use 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.
Observations are 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.
The independent tallies are compared, and a correlational analysis is usually conducted. The scientific data analysis has high inter-rater reliability if the results are similar and a high positive correlation is found.
Semi-unstructured interviews use open-ended and closed-ended questions to obtain quantitative and qualitative data. The analysis involves taking notes from the interview transcripts, which are later categorised by themes; this process is called thematic 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.
The thematic analysis allows the researcher and the reader to understand the phenomena in depth. Furthermore, it can be classified as a scientific data analysis technique, relying on an evidence-based interpretation of the themes, concepts and patterns.
Personal records such as diaries and letters provide qualitative information. The technique of scientific data analysis is quite different from that used for quantitative 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 called content analysis.
Content analysis is an analysis method used to identify words, themes, and concepts in qualitative data, such as diaries, and follows a similar protocol to thematic analysis.
However, content analysis quantifies words, themes, and concepts to understand their meaning and relationship. The statistical tests used for quantitative data can then be used.
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?
Q1. What is your age?
Q2. On a scale of 1 - 5 (most likely to very unlikely), are you to recommend the app to your friends?
Q3. How many hours do you spend on social media daily?
Q1 collects ratio data; Q2 collects ordinal data, and Q3 collects ratio data.
Scientific Data Analysis - Key takeaways
- The data analysis scientific method definition is a standardised process that accurately and objectively analyses data from research observed in the study (i.e. empirical).
- 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. The study's hypothesis can also affect scientific data collection and analysis.
- Levels of measurement in statistics describe and classify types of variables and how to measure them. There are four levels of measurement: nominal, ordinal, ratio and interval data.
- Data analysis and interpretation of case studies depend on the researcher's research method, but some typical analysis techniques are thematic and content analysis.
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