How to analyse quantitative data for a dissertation

Branches of quantitative data analysis

As we have discussed, the basis of the quantitative data analysis lies in the statistical analysis method, and it has 2 major types.

A.       Descriptive Statistics

Descriptive statistics is focused on samples. It leverages visual tools like graphs and tables. You will use tools like medians, modes, and variance to segment and characterise data samples according to their properties.

There are 4 types of descriptive statistics

1. Central tendency

Central tendency is a way to describe the middle of a data set. The three most common measures of central tendency are the mean, median, and mode.

1. Dispersion

Dispersion is a way to describe how spread out a data set is. The two most common measures of dispersion are the range and standard deviation.

• Skewness

Skewness is a way to describe the symmetry of a data set. A data set is said to be skewed if it has a long tail in one direction or the other.

1. Kurtosis

Kurtosis is a way to describe the peakedness of a data set. A data set is kurtotic if it has a sharp peak in the middle and long tails on either side.

B.      Inferential Statistics

Inferential statistics allow us to predict a population based on a sample. It's different from descriptive statistics, which involves summarising data.

Some popular examples of inferential statistical techniques include:

• Regression analysis
• Chi-square test
• T-test

And they all have one thing in common, all rely on information from a sample to make inferences about a population.

Population and sample, what are they?

Before moving on, let me clarify the confusing lingo first.

The population is what you might think it is, a group of people, things, organisations or pretty much everything. For example, all athlete in the UK is a population in statistics.

The sample is a smaller chunk of that population, which you will use to test, observe, analyse and create a narrative around. For example, athletes in the UK aged 30 to 32 years of age is a sample of the population of athletes in the UK.

Recap of sample and population

Now that we've gone over the definition of population and sample let's do a quick recap. The population is the entire group you want to study, while the sample is the smaller subset of that larger group that you can access.

So think of it this way: if the population is empire state building, then your sample would be just one floor of the building.

Quantitative data method

Collecting quantitative data is all about gathering numerical information.

• Surveys
• Experiments
• Secondary data analysis
• Observational studies
• Quasi-experimental designs

You can leverage any of these methods depending on the type of study you are conducting. You will love the data collection process if you are into numbers and stuff.

Also Read: What is the main difference between qualitative and quantitative research methods?

How to choose the right method of analysis for your data?

This is the most important question you can ask, and it will depend on the research you are conducting. Following are the methods you can use to characterise and analyse data.

Descriptive statistics: Descriptive statistics are used to summarise your data and can be used to describe the distribution of your data.

Inferential statistics: Inferential statistics are used to make predictions or inferences about a population based on a sample.

Linear regression: Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.

Logistic regression: Logistic regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables where the dependent variable is binary.

ANOVA: ANOVA is a statistical method used to test for differences between two or more groups.

The benefits of using a statistical software package to analyse your data

Data analysis is for sure an intriguing subject, but with software packs, it can be less daunting, and you can process your data with clicks. Here are the 9 benefits a statistical software pack will offer while processing your quantitative data.

A statistical software package can help you to

1. Save time by performing complex calculations quickly and accurately.
2. Easily analyse large data sets and spot trends that would be difficult to identify manually.
3. Test hypotheses and compare different data sets side-by-side.
4. Produce powerful visualisations so you can communicate your findings more effectively.
5. Collaborate with other researchers more easily, as most packages allow you to share your work electronically.
6. Keep track of your work, as most packages allow you to save your work in a variety of formats (e.g., text, Excel, PDF).
7. A statistical software package can be a valuable teaching tool, as it can help students to better understand complex concepts through interactive visualisations and simulations.
8. You can use it for various applications, including data analysis, predictive modelling, simulation, and optimisation.
9. A statistical software package can be valuable in any field that relies on data analysis, including business, finance, medicine, and the social sciences.

Tips for ensuring that your analysis is accurate and reliable

Let me tell you the best tips at the end to ensure the accuracy and reliability of quantitative data analysis

• Define the problem
• Collect data
• Clean and prepare data
• Exploratory data analysis
• Model data
• Evaluate model
• Refine model
• Deploy model
• Monitor results
• Iterate

Conclusion

So, what is quantitative data? Quantitative data is numerical information that has been collected and organised systematically. You can use this data type to answer questions about a population or test hypotheses. It's important to note that quantitative data can only be analysed using mathematical methods.

To perform any statistical analysis, you first need to understand the question you're trying to answer. Once you have identified the question, you can select an appropriate method for answering it. Many techniques are available for analysing quantitative data, but not all will apply to your dissertation question. To choose the right technique, you need to understand the theory behind it and how it works in practice. If this sounds like Greek (or maths) to you, don't worry – we have plenty of resources to help!

The best way to learn is by doing, so start your analysis today! When it comes time to write up your results, remember Premier Dissertations for expert assistance with your dissertation methodology and results chapters too!