Central tendency: Mean, median and mode

Measures of central tendency help you find the middle, or the average, of a data set. The 3 most common measures of central tendency are the mode, median, and mean.

  • Mode: the most frequent value.
  • Median: the middle number in an ordered data set.
  • Mean: the sum of all values divided by the total number of values.

In addition to central tendency, the variability and distribution of your data set is important to understand when performing descriptive statistics.

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Levels of measurement: Nominal, ordinal, interval, ratio

Levels of measurement, also called scales of measurement, tell you how precisely variables are recorded. In scientific research, a variable is anything that can take on different values across your data set (e.g., height or test scores).

There are 4 levels of measurement:

  • Nominal: the data can only be categorized
  • Ordinal: the data can be categorized and ranked
  • Interval: the data can be categorized, ranked, and evenly spaced
  • Ratio: the data can be categorized, ranked, evenly spaced, and has a natural zero.

Depending on the level of measurement of the variable, what you can do to analyze your data may be limited. There is a hierarchy in the complexity and precision of the level of measurement, from low (nominal) to high (ratio).

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An introduction to descriptive statistics

Descriptive statistics summarize and organize characteristics of a data set. A data set is a collection of responses or observations from a sample or entire population.

In quantitative research, after collecting data, the first step of data analysis is to describe characteristics of the responses, such as the average of one variable (e.g., age), or the relation between two variables (e.g., age and creativity).

The next step is inferential statistics, which are tools that help you decide whether your data confirms or refutes your hypothesis and whether it is generalizable to a larger population.

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Designing and analyzing Likert scales

A Likert scale is a rating scale used to assess opinions, attitudes, or behaviors. Likert scales are popular in survey research because they allow you to easily operationalize personality traits or perceptions.

To collect data, you present participants with Likert-type questions or statements and a continuum of possible responses, usually with 5 or 7 items. Each item is given a numerical score so that the data can be analyzed quantitatively.

How frequently do you buy energy efficient products?
NeverRarelySometimesOftenAlways
My manager plays an active role in my professional development and advancement.
Very strongly disagreeStrongly disagreeDisagreeNeither disagree nor agreeAgreeStrongly agreeVery strongly agree
How satisfied are you with the online shopping return policies at Company X?
Very dissatisfiedSomewhat dissatisfiedNeither satisfied nor dissatisfiedSomewhat satisfiedVery satisfied

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A guide to operationalization

Operationalization means turning abstract concepts into measurable observations. Although some concepts, like height or age, are easily measured, others, like spirituality or anxiety, are not.

Through operationalization, you can systematically collect data on processes and phenomena that aren’t directly observable.

Operationalization example
The concept of social anxiety can’t be directly measured, but it can be operationalized in many different ways. For example:

  • self-rating scores on a social anxiety scale
  • number of recent behavioral incidents of avoidance of crowded places
  • intensity of physical anxiety symptoms in social situations

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An introduction to qualitative research

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research, which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

Qualitative research question examples
  • How does social media shape body image in teenagers?
  • How do children and adults interpret healthy eating in the UK?
  • What factors influence employee retention in a large organization?
  • How is anxiety experienced around the world?
  • How can teachers integrate social issues into science curriculums?

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An introduction to quantitative research

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research, which involves collecting and analyzing non-numerical data (e.g. text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

Quantitative research question examples
  • What is the demographic makeup of Singapore in 2020?
  • How has the average temperature changed globally over the last century?
  • Does environmental pollution affect the prevalence of honey bees?
  • Does working from home increase productivity for people with long commutes?

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A step-by-step guide to data collection

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem.

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

  • The aim of the research
  • The type of data that you will collect
  • The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

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Sampling bias: What is it and why does it matter?

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others. It is also called ascertainment bias in medical fields.

Sampling bias limits the generalizability of findings because it is a threat to external validity, specifically population validity. In other words, findings from biased samples can only be generalized to populations that share characteristics with the sample.

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