What does the quantitative data analysis process consist of? Quantitative data analysis is a five step process. The first step is to define your research questions and hypotheses clearly Dissertation data analysis is one of Dissertation Genius’s core competencies; we have PhD-level statistical consultants on our team as well as over 22 years of experience in all types of data analysis. For more details on our qualitative analysis services, visit our Qualitative Analysis blogger.comted Reading Time: 1 min Panel Data Study Degree: BSc International Business and Economics Supervisor: blogger.comer Munjal for his continuous support during my dissertation in terms of his patience, motivation and vast knowledge. (, ) formed the basis for most of the analysis within this Neo-classical model. This model explains that steady economic
Quantitative Data Analysis - Dissertation Genius
By: Derek Jansen MBA and Kerryn Warren PhD December Quantitative data analysis is one of those things that often strikes fear in students. Statistical analysis methods form the engine that powers quantitative analysis, and these methods can vary from pretty basic calculations dissertation quantitative data analysis example, averages and medians to more sophisticated analyses for example, correlations and regressions.
Sounds like gibberish? See how Grad Coach can help you In other words, depending on your research questions, aims and objectives, dissertation quantitative data analysis. Well, before I can explain that, we need to take a quick detour to explain some lingo.
To understand the difference between these two branches of statistics, you need to understand two important words. These words are population and sample. First up, population. For example, if you were interested in researching Tesla owners in the US, then the population would be all Tesla owners in the US. This smaller group of accessible people whose data you actually collect is called your sample.
In other words, the population is the full chocolate cakewhereas the sample is a slice of that cake. Well, descriptive statistics focus on describing the samplewhile inferential statistics aim to make predictions about the population, based on the findings within the sample.
In other words, we use one dissertation quantitative data analysis of statistical methods — descriptive statistics — to investigate the slice of cake, and another group of methods — inferential statistics — to draw conclusions about the entire cake. There I go with the cake analogy again…. Descriptive statistics serve a simple but critically important role in your research — to describe your data set — hence the name.
In other words, they help you understand the details of your sample. But, that said, depending on your research objectives and research questions, they may be the only type of statistics you use. Feeling a bit confused? On the left-hand side is the data set. This details the bodyweight of a sample of 10 people. On the right-hand side, we have the descriptive statistics. First, we can see that the mean weight is In other words, the average weight across the sample is Next, we can see that the median is very similar to the mean the average.
This suggests that this data set has a reasonably symmetrical distribution in other words, a relatively smooth, centred distribution of weights, clustered towards the centre. In terms of the modethere is no mode in this data set. If there were two people who were both 65 kilograms, for example, then the mode would be Next up is the standard deviation. We can see this quite easily by looking at the numbers themselves, which range from 55 dissertation quantitative data analysis 90, which is quite a stretch from the mean of And lastly, dissertation quantitative data analysis, the skewness of This makes sense since the mean and the median are slightly different.
As you can see, these descriptive statistics give us some useful insight dissertation quantitative data analysis the data set. Also, keep in mind that this is not a list of all possible descriptive statistics — just the most common ones.
Simply put, descriptive statistics are really importanteven though the statistical techniques used are fairly basic. All too often at Grad Coach, we see students skimming over the descriptives in their eagerness to get to the more exciting inferential methods, and then landing up with some very flawed results. As I mentioned, while descriptive statistics are all about the details of your specific data set — your sample — inferential statistics aim to make inferences about the population.
What kind dissertation quantitative data analysis predictions, you ask? Well, there are two common types of predictions that researchers try to make using inferential stats:. In other words, inferential statistics when done correctlyallow you to connect the dots and make predictions about what you expect to see in the real world population, based on what you observe in your sample data. For this reason, inferential statistics are used for hypothesis testing — in dissertation quantitative data analysis words, to test hypotheses that predict changes or differences.
First up are T-Tests. In other words, do they have significantly different means, standard deviations and skewness. This type of testing is very useful for understanding just how similar or different two groups of data are.
Next, we have correlation analysis, dissertation quantitative data analysis. This type of analysis assesses the relationship between two variables.
In other words, if one variable increases, does the other variable also increase, decrease or stay the same. For dissertation quantitative data analysis, if the average temperature goes up, do average ice creams sales increase too? Lastly, we have regression analysis — this is quite similar to correlation in that it assesses the relationship between variables, but it goes a step further to understand cause and effect between variables, not just whether they move together.
In other words, does the one variable actually cause the other one dissertation quantitative data analysis move, or do they just happen to move together naturally thanks to another force? I hear you. In other words, the results tend to cluster together in a diagonal line from bottom left to top right. As I mentioned, dissertation quantitative data analysis, these are are just a handful of inferential techniques — there are many, many more.
Importantly, each statistical method has its own assumptions and limitations. For example, some methods only work with normally distributed parametric data, while other methods are designed specifically for non-parametric data. To choose the right statistical methods, you need to think about two important factors :. Well, because different statistical methods and techniques require different types of data. Once you have this, you can then check which statistical methods would support your data types here.
Another important factor to consider is the shape of your data. Specifically, does it have a normal distribution in other words, dissertation quantitative data analysis, is it a bell-shaped curve, centred in the middle or is it very skewed to the left or the right? Again, different statistical techniques work for different shapes of data — some are designed for symmetrical data while others are designed for skewed data. This is another reminder of why descriptive statistics are so important — they tell you all about the shape of your data.
The next thing you need to consider is your specific research questions, as well as your hypotheses if you have some. The nature of your research questions and research hypotheses will heavily influence which statistical methods and techniques you should use. For example, if you just want to assess the means averages and medians centre points of variables in a group of people.
Never shoehorn a specific statistical technique into your research just because you like it or have some experience with it. This post is part of our research writing mini-course, which covers everything you need to get started with your dissertation, thesis or research project.
This is beautiful…. especially for non-statisticians. I have skimmed through but I wish to read again. and please include me in other articles of the same nature when you do post. I am interested. I am sure, I could easily learn from you and get off the fear that I have had in the past.
Thank you sincerely. Your article is so good! However, I am still a bit lost. I am doing a secondary research on Gun control in the US and increase in crime rates and I am not sure which analysis dissertation quantitative data analysis I should use? Glad to read this article. Thanks for sharing. Thank you so much. This is a very good foundation and intro into quantitative data analysis. You have a very impressive, simple but concise explanation of data analysis for Quantitative Research here.
This is a God-send link for me to appreciate research more. Thank you so dissertation quantitative data analysis Avery good presentation followed by the write up. yes dissertation quantitative data analysis simplified statistics to make sense even to a layman like me. Thank so much keep it up. The presenter did ell too. i would like more of this for Qualitative and exhaust more of the test example like the Anova.
The video with the accompanying article is super helpful to demystify this topic, dissertation quantitative data analysis. Very well done. It is well defined information and thanks for sharing, dissertation quantitative data analysis.
It helps me a lot in understanding the statistical data. An outstanding, well explained and helpful article. This will help me so much with my data analysis for my research project. Thank you! wow this has just simplified everything i was scared of how i am gonna analyse my data but thanks to you i will be able to do so. Your email address will not be published.
SPSS: How To Perform Quantitative Data Analyses For Bachelor's Research? 5 Basic Analysis Methods
, time: 8:32Quantitative : Dissertation Editor
When taking on a quantitative dissertation, there are many different routes that you can follow. We focus on three major routes that cover a good proportion of the types of quantitative dissertation that are carried out. We call them Route #1: Replication-based dissertations, Route #2: Data-driven dissertations and Route #3: Theory-driven dissertations. Each of these three routes reflects a very Quantitative Data Analysis. In quantitative data analysis you are expected to turn raw numbers into meaningful data through the application of rational and critical thinking. Quantitative data analysis may include the calculation of frequencies of variables and differences between variables. A quantitative approach is usually associated with finding evidence to either support or reject hypotheses you have The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual
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