In this video, we're going to jump into the often confusing world of quantitative data analysis. We're going to explore what quantitative data analysis is, some of the most popular analysis methods and how to choose the right methods for your research. We'll also cover some useful tips, as well as common pitfalls to avoid when you're undertaking quantitative analysis.
So grab a cup of coffee, grab a cup of tea, whatever works for you and let's jump into it! Hey! Welcome to Grad Coach TV - where we demystify and simplify the oftentimes intimidating world of academic research my name is Emma and today we're going to unwrap the topic of quantitative data analysis if you're new here be sure to hit that subscribe button for more videos covering all things research-related also if you're looking for hands-on help with your research check out our one-on-one coaching services where we help you through your dissertation thesis or research project step by step it's basically like having a professor in your pocket whenever you need it so if that sounds interesting to you you can learn more and book a free consultation with a friendly coach at www all right with that out of the way let's jump into it quantitative data analysis is one of those things that often strikes fear into students it's totally understandable quantitative analysis is a complex topic full of daunting lingo like medians modes correlations and regression suddenly we're all wishing we'd paid a little more attention in math class now the good news is that while quantitative data analysis is a mammoth topic gaining a working understanding of the basics isn't that hard even for those of us who avoid numbers and math at all costs in this video we'll break quantitative analysis down into simple bite-sized chunks so you can get comfy with the core concepts and approach your research with confidence so let's start with the most basic question what exactly is quantitative data analysis despite being quite a mouthful quantitative data analysis simply means analyzing data that's numbers based or data that can be easily converted into numbers without losing any meaning for example category based variables like gender ethnicity or native language can all be converted into numbers without losing meaning for example english could equal one french could equal two and so on this contrasts against qualitative data analysis where the focus is on words phrases and expressions that can't be reduced to numbers if you're interested in learning about qualitative analysis we've got a video covering that as well i'll include a link below so the next logical question is what is quantitative analysis used for well quantitative analysis is generally used for three purposes first it's used to measure differences between groups for example average height differences between different groups of people second it's used to assess relationships between variables for example the relationship between weather temperature and voter turnout and third it's used to test hypotheses in a scientifically rigorous way for example a hypothesis about the impact of a certain vaccine again this contrasts with qualitative analysis which can be used to analyze people's perceptions and feelings about an event or situation in other words things that can't be reduced to numbers so how does quantitative analysis work you ask well since quantitative data analysis is all about analyzing numbers it's no surprise that it involves statistics statistical analysis methods form the engine that powers quant analysis these methods can vary from pretty basic calculations for example averages and medians to more sophisticated analyses for example correlations and regressions sounds like a bunch of gibberish don't worry we will explain all of that in this video importantly you don't need to be a statistician or a math whiz to pull off a good quantitative analysis we'll break down all the technical mumbo jumbo in this video so let's start by taking a look at the two main branches of quantitative analysis as i mentioned quantitative analysis is powered by statistical analysis methods there are two main branches of statistical methods that are used descriptive statistics and inferential statistics in your research you might only use descriptive statistics or you might use a mix of both depending on what you're trying to figure out in other words depending on your research questions aims and objectives i'll explain how to choose your methods later in this video so what are descriptive and inferential statistics 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 step population in statistics the population is the entire group of people or animals or organizations or whatever that you're interested in researching for example if you were interested in researching tesla owners in the us then the population would be all tesla owners in the united states however it's extremely unlikely that you're gonna be able to interview or survey every single tesla owner in the u.
s realistically you'll only get access to a few hundred or maybe a few thousand owners using an online survey this smaller group of accessible people whose data you actually collect is called your sample so to recap the population is the entire group of people you're interested in and the sample is the subset of that population that you can actually get access to in other words the population is the full chocolate cake whereas the sample is just a slice of that cake can you see what i've got on my mind anyhow why is this sample population thing important well descriptive statistics focuses on describing the sample while inferential statistics aim to make predictions about the population based on the findings within the sample in other words we use one group 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 and there i go with the cake analogy again but to be fair i always have chocolate on my mind so with that out of the way let's take a closer look at each of these branches in more detail starting with descriptive statistics 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 unlike inferential statistics which we'll get to later descriptive statistics don't aim to make inferences or predictions about the entire population they're purely interested in the details of your specific sample when you're writing up your analysis descriptive statistics are the first set of stats you'll cover before moving on to inferential statistics but depending on your research objectives and research questions they may be the only type of statistics that you use we'll explore that a little later so what kind of statistics are usually covered in this section well some common statistical tests used in this branch include the following the mean this is simply the mathematical average of a range of numbers nothing too complicated here next is the median this is the midpoint in a range of numbers when the numbers are all arranged in order if the data set makes up an odd number then the median is the number right in the middle of the set if the data set makes up an even number then the median is the midpoint between the two middle numbers next up is the mode this is simply the most commonly repeated number in the data set then we have standard deviation this metric indicates how dispersed a range of numbers is in other words how close all the numbers are to the mean the average in cases where most of the numbers are quite close to the average the standard deviation will be relatively low conversely in cases where the numbers are scattered all over the place the standard deviation will be relatively high lastly we have skewness as the name suggests skewness indicates how symmetrical a range of numbers is in other words do they tend to cluster into a smooth bell curve shape in the middle of the graph this is called a normal or parametric distribution or do they lean to the left or right this is called a non-normal or non-parametric distribution okay are you feeling a bit confused let's look at a practical example on the left hand side is the data set this data set details the body weight in kilograms of a sample of 10 people on the right hand side we have the descriptive statistics for this data set let's take a look at each of them first we can see that the mean weight is 72. 4 kilograms in other words the average weight across the sample is 72. 4 kilograms pretty straightforward 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 center distribution of weights clustered towards the center moving on to the mode well there is no mode in this data set this is because each number presents itself only once and so there cannot be a most common number if hypothetically there were two people who were both 65 kilograms then the mode would be 65.
next up is the standard deviation 10. 6 indicates that there's quite a wide spread of numbers we can see this quite easily by just looking at the numbers which range from 55 to 90. this is quite a stretch from the mean of 72.
4 so we would expect the standard deviation to be well above zero and lastly let's look at the skewness a result of negative 0. 2 tells us that the data is very slightly negatively skewed in other words it has a very slight lean this makes sense since the mean and the median are only slightly different as you can see these descriptive statistics give us some useful insight into the data set of course this is a very small data set only 10 records so we can't read into these statistics too much but hopefully this example helps you understand how these statistics play out in reality also keep in mind that this is not a list of all possible descriptive statistics just the most common ones so at this point you might be wondering but why do these matter well while these descriptive statistics are all fairly basic they're important for a few reasons firstly they help you get both a macro and micro level view of your data they help you understand both the big picture and the finer details secondly they help you spot potential errors in the data for example if an average is way higher than you'd expect or responses to a question are highly varied this can act as a warning sign that you need to double check the data and lastly these descriptive statistics help inform which inferential statistical methods you can use as those methods depend on the shape of the data we'll explore this a little bit more later on simply put descriptive statistics are really important even though the statistical methods used are pretty basic all too often at grad coach we see students rushing past the descriptives in their eagerness to get to the more exciting inferential methods and then landing up with some very flawed results don't be a sucker give your descriptive statistics all the love and attention they deserve all right now that we've looked at descriptive stats let's move on to the second branch of quantitative analysis inferential statistics 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 in other words you'll use inferential statistics to make predictions about what you'd expect to find in the full population what kind of predictions you ask well generally speaking there are two common types of predictions that research try to make using inferential stats firstly predictions about differences between groups for example height differences between children grouped by their favorite sport and secondly relationships between variables for example the relationship between body weight and the number of hours a week a person does yoga in other words inferential statistics when done correctly allow you to connect the dots and make predictions about what you'd 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 other words to test hypotheses that predict changes or differences of course when you're working with inferential statistics the composition of your sample is really important in other words if your sample doesn't accurately represent the population you're researching then your findings won't necessarily be very useful for example if your population of interest is a mix of 50 male and 50 female but your sample is 80 male you can't make inferences about the population based on your sample since it's not representative this area of statistics is called sampling but we won't go down that rabbit hole here it's a deep one we'll save that for another video so what kind of statistics are usually covered in this section well there are many many different statistical analysis methods within the inferential branch and it would be impossible for us to discuss them all here so we'll just take a look at some of the most common inferential statistical methods so that you have a solid starting point first up are t-tests t-tests compare the means the averages of two groups of data to assess whether they are different to a statistically significant extent in other words to see whether they have significantly different means standard deviations and skewness for example you might want to compare the mean blood pressure between two groups of people one that has taken a new medication and one that hasn't to assess whether they are significantly different simply looking at the two means is not enough to draw a conclusion you need to assess whether the differences are statistically significant and that's what t-tests allow you to do right next up is anova anova stands for analysis of variance this test is similar to a t-test in that it compares the means of various groups but anova allows you to analyze multiple groups not just two so it's basically a t-test but on steroids next we have correlation 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 example if the average temperature goes up do average ice cream sales increase too we'd expect some sort of relationship between these two variables intuitively but correlation analysis allows us to measure that relationship scientifically lastly we have regression analysis regression analysis is similar to correlation in that it assesses the relationship between variables but it goes a step further to understand the cause and effect between variables not just whether they move together in other words does the one variable actually cause the other one to move or do they just happen to move together naturally thanks to another force just because two variables correlate doesn't necessarily mean that one causes the other to make this all a little more tangible let's take a look at an example of correlation in action here's a scatter plot demonstrating the correlation or the relationship between weight and height intuitively we'd expect there to be some sort of relationship between these two variables which is what we see in this scatter plot in other words the results tend to cluster together in a diagonal line from bottom left to top right the more tightly the results cluster together to form a line in any direction the more correlated they are and therefore the stronger the relationship between the variables as i mentioned these are just a handful of inferential methods there are many many more importantly each statistical method has its own assumptions and limitations for example some methods only work with normally distributed or parametric data while other methods are designed specifically for data that are not normally distributed and that's exactly why descriptive statistics are so important they're the first step to knowing which inferential methods you can and can't use of course this all begs the question how do i choose the right quantitative analysis methods for my research well that's exactly what we'll look at next now that we've looked at some of the most common statistical methods used within quantitative analysis let's look at how you go about choosing the right tool for the job to choose the right statistical methods for your research you need to think about two important factors one the type of quantitative data you have specifically level of measurement and the shape of the data and two your research questions and hypotheses let's take a closer look at each of these the first thing you need to consider is the type of data you've collected or the data you will collect by data types i'm referring to the four levels of measurement namely nominal ordinal interval and ratio if you're not familiar with this lingo you should hit the pause button real quick and go check out our post over on the grad coach blog that explains each of these levels of measurement i'll include the link below okay so why does this matter well because different statistical methods require different types of data this is one of the assumptions i mentioned earlier every method has its assumptions regarding the type of data for example some methods work with categorical data like yes or no type questions while others work with numerical data like age weight or income if you try to use a statistical method that doesn't support the data type you have your results will be largely meaningless so make sure you have a clear understanding of what types of data you've collected or will collect once you have this you can then check which statistical methods support your data types i'll include a link below the video that explains which methods support which data types now if you haven't collected your data yet you can of course reverse engineer the process and look at which statistical methods would give you the most useful insights and then design your data collection strategy around this to ensure that you collect the correct data types another important factor to consider is the shape of your data specifically does it have a normal distribution in other words is it a bell-shaped curve centered in the middle or is it very skewed to the left or right again different statistical methods 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 since 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 you should use if you're just interested in understanding the attributes of your sample as opposed to the entire population then descriptive statistics might be all you need for example if you just want to assess the means or averages and the medians or center points of variables in a group of people descriptives will do the trick on the other hand if you aim to understand differences between groups or relationships between variables and to infer or predict outcomes in the population then you'll likely need both descriptive statistics and inferential statistics so it's really important to get very clear about your research aims and research questions as well as your hypotheses before you start looking at which statistical methods to use never shoehorn a specific method into your research just because you like it or have experience with it your choice of methods must align with all the factors we've covered here all right now that we've looked at what quantitative analysis is the two main branches of statistics and how to choose the right methods for your research let's recap and bring it all together we've covered a lot in this video well done on making it this far let's recap on the key points we've looked at first we asked the question what is quantitative data analysis as we discussed quantitative analysis is all about analyzing number based data which can include both categorical and numerical data these data are analyzed using statistical methods the two main branches of statistics are descriptive statistics and inferential statistics descriptives describe your sample the slice of the cake while inferentials make predictions about what you'll find in the population the full cake based on what you've observed in the sample as we saw common descriptive statistical metrics include the mean the median the mode standard deviation and skewness on the inferential side we looked at t tests anovas correlation analysis and regression analysis all of which can help you make predictions about the population lastly we asked the important question how do i choose the right statistical methods as we discussed to choose the right statistical methods you need to consider the type of data you're working as well as your research questions and hypotheses remember in this video we've only looked at a handful of the most common quantitative methods there are many many more so be sure to check out the grad coach blog as well as the other links below this video to get a fuller picture of what all's on offer in terms of statistical methods also if you'd like us to cover any of the methods in more detail be sure to leave a comment below alright that wraps it up for today if you enjoyed the video hit that like button and leave a comment if you have any questions also be sure to subscribe to the grad coach channel for more research related content lastly if you need a helping hand with your research check out our private coaching service where we work with you on a one-on-one basis chapter by chapter to help you craft a winning dissertation thesis or research project if that sounds interesting to you book a free consultation with a friendly coach at www www. bradcoach.
