An easy introduction to deductive reasoning

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning, where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic or top-down reasoning.

Note: Deductive reasoning is often confused with inductive reasoning. However, in inductive reasoning, you draw conclusions by going from the specific to the general.

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Inductive Reasoning | Types, Examples, Explanation

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you go from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

Note: Inductive reasoning is often confused with deductive reasoning. However, in deductive reasoning, you make inferences by going from general premises to specific conclusions.

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A beginner’s guide to triangulation in research

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research, but it’s also commonly applied in quantitative research. If you decide on mixed methods research, you’ll always use methodological triangulation.

Examples: Triangulation in different types of research
  • Qualitative research: You conduct in-depth interviews with different groups of stakeholders, such as parents, teachers, and children.
  • Quantitative research: You run an eye-tracking experiment and involve three researchers in analyzing the data.
  • Mixed methods research: You conduct a quantitative survey, followed by a few (qualitative) structured interviews.

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Observer bias

Observer bias happens when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It often affects studies where observers are aware of the research aims and hypotheses. Observer bias is also called detection bias or ascertainment bias.

Observer bias is particularly likely to occur in observational studies. But it can also affect other types of research where measurements are taken or recorded manually.

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What is the geometric mean?

The geometric mean is an average that multiplies all values and finds a root of the number. For a dataset with n numbers, you find the nth root of their product. You can use this descriptive statistic to summarize your data.

The geometric mean is an alternative to the arithmetic mean, which is often referred to simply as “the mean.” While the arithmetic mean is based on adding values, the geometric mean multiplies values.

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How to find and remove outliers

Outliers are extreme values that differ from most other data points in a dataset. They can have a big impact on your statistical analyses and skew the results of any hypothesis tests.

It’s important to carefully identify potential outliers in your dataset and deal with them in an appropriate manner for accurate results.

There are four ways to identify outliers:

  1. Sorting method
  2. Data visualization method
  3. Statistical tests (z scores)
  4. Interquartile range method

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A guide to data cleansing

Data cleansing involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of whatever is being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleansing is also called data cleaning or data scrubbing.

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Attrition bias in research

Attrition is participant dropout over time in research studies. It’s also called subject mortality, but it doesn’t always refer to participants dying!

Almost all longitudinal studies will have some dropout, but the type and scale of the dropout can cause problems. Attrition bias is the selective dropout of some participants who systematically differ from those who remain in the study.

Attrition bias is especially problematic in randomized controlled trials for medical research.

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Understanding demand characteristics

In research, demand characteristics are cues that might indicate the study aims to participants. These cues can lead participants to change their behaviors or responses based on what they think the research is about.

Demand characteristics are problematic because they can bias your research findings. They commonly occur in psychology experiments and social sciences studies because these involve human participants.

It’s important to consider potential demand characteristics in your research design and deal with them appropriately to obtain valid results.

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