Free Tools & Resources for UX Researchers

Resources for Researchers

Getting started in research, of any kind, can be overwhelming. User experience research is no exception. This industry is quickly growing and evolving and keeping up may seem daunting. While this is true, many of the elements of UX are rooted in social science, mathematics, and economics. Knowing this, you’ll have a strong foundation to start with as you progress in your career.

Whether you’re just looking for a refresher on the basics or just getting started with your profession and diving into UX, this list of free tools and resources will help point you in the right direction.

Please note, this will be an ever-evolving list Be sure to check back from time to time.

To help you stay on top of your data analysis here are links to online calculators and qualitative and quantitative resources.

we will continue to add to this list.

Tools for Data Analysis

Quantitative Analysis & Online Calculators

Pearson Correlation Coefficient

The Pearson correlation coefficient is used to measure the strength of a linear association between two variables, where the value r= 1 means a perfect positive correlation and the value r= -1 means a perfect negative correlation. So, for example, you could use this test to find out whether people’s height and weight are correlated (for example, the taller people are, the heavier they’re likely to be).

Requirements for Pearson’s correlation Coefficient

• Scale of measurement should be interval or ratio
• Variables should be approximately normally distributed
• The association should be linear
• There should be no outliers in the data

Link for Correlation Calculator

Standard Deviation

Standard deviation is a statistical measure of diversity or variability in a data set. A low standard deviation indicates that data points are generally close to the mean or the average value. A high standard deviation indicates greater variability in data points or higher dispersion from the mean

Link for Standard Deviation Calculator

Significance Calculator

Can be used for data that is:

1. Categorical

2. Continuous

3. Statistical distributions and interpreting P values

4. Random Numbers

5. Chemical and radiochemical data

Link for Significance Calculator

Percentage Change

Use for:

1. What is (Blank)% Percentage of (Blank)?

2. (Blank) is what percentage of (Blank)?

3. What is the percentage increase/decrease?

Link for Percentage Calculator

Fisher-Exact Test

Fisher’s exact test is a statistical significance test used in the analysis of contingency tables. Although in practice it is employed when sample sizes are small, it is valid for all sample sizes

Link for Fisher-Exact Test Calculator

Curious about what analysis method you’ll need?

Here’s a quick overview of the more common (and a few not-so-common) data analysis techniques.

T- Test

• Analyze the means between sets as in differences in group means.
• Use a t-test to compare the mean of two given samples. Like a z-test, a t-test also assumes a normal distribution of the sample. When we don’t know the population parameters (mean and standard deviation), we use t-tests.

Z-test

• Analyze means between data sets.
• Parameters
• Population standard deviation is known.
• The sample size of over 30.
• We use this test to validate a hypothesis that states the sample belongs to the same population.

ANOVA

• Use an analysis of variance (ANOVA) to compare three or more samples with a single test.
• One-way ANOVA: is used to compare the means of two or more independent groups to determine whether there is statistical evidence that the associated population means are significantly different.

MANOVA

• Tests the effect of one or more independent variables on two or more dependent variables.
• MANOVA can also detect the difference in correlation between dependent variables given the groups of independent variables

Chi-square

• Compare proportions
• Example: male or female and Democrat or Republican.
• Compares the proportion of counts in each category with the expected proportions.

Spearman rank correlation

• A non-parametric test is used to measure the degree of association between two variables. This is an alternative to Pearson’s correlation
• Use Spearman rank correlation when you have two ranked variables, and you want to see whether the two variables covary; whether, as one variable increases, the other variable tends to increase or decrease.

Pearson’s R

• Also called the Pearson correlation coefficient
• Is a numerical summary of the strength of the linear association between the variables.
• For example, if the variables tend to go up and down together, the correlation coefficient will be positive. If the variables tend to go up and down in opposition to the low values of one variable associated with the high values of the other, the correlation coefficient will be negative.

Regression

• Determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables. Or it can predict the effects of the independent variable on the dependent one.

Power

• Find the sample size needed
• Use G*Power
• You need to know about population, confidence, and margin of error to determine size.

Max difference

• Best-worst scaling
• This is an approach for obtaining preference/importance scores for multiple items.

Conjoint analysis

• Describes the product/service with multiple attributes.

Qualitative Data Analysis

Word Cloud

• Free word cloud generator

Chrome Extensions

ThoughtSpot

• ThoughtSpot for Google Sheets™ lets anyone use search to create charts and visualizations from spreadsheet data.

Scholarcy

• AI tool that summarizes high-volume research papers in seconds.

Web Collector for MAXQDA

• Easily collect whole web pages or selected parts for import and analysis

Mendeley web importer

• Allows academic researchers to save PDF copies of references to their libraries

Cite This For Me

• Free referencing application that helps academic researchers

Search Highlighter

• Works as a keyword finder

Be sure to check back in for updates. As we find more free tools and resources that benefit user experience researchers we’ll add them to the list.