GROUP 6 A6 1 Describe Quantitative data analysis methods Quantitative data analysis simply means analysing data that is numbers based (as opposed to words based), or data that can be easily “converted.
GROUP : A6 Describe Quantitative data analysis methods - Quantitative data analysis simply means analysing data that is numbers-based (as opposed to words-based), or data that can be easily “converted” into numbers without losing any meaning For example, category-based variables such as gender, ethnicity, or native language could all be “converted” into numbers without losing meaning - Quantitative data analysis is typically used to measure differences between groups (for example, the popularity of different clothing colours), relationships between variables (for example, the relationship between weather temperature and voter turnout), and to test in a scientifically rigorous way This contrasts with qualitative data analysis, which can be used to analyse people’s perceptions and feelings about an event or situation To learn more about the differences between qualitative and quantitative research, check out this article - Since quantitative data analysis is all about analysing numbers, it’s no surprise that it involves statistics Statistical analysis methods and techniques are the engine that powers quantitative data analysis, and these methods and techniques can vary from pretty basic calculations (for example, averages and medians) through to more sophisticated analyses (for example, correlations and regressions) The techniques/methods : Some common statistical techniques used in this branch include: • Mean – this is simply the mathematical average of a range of numbers • Median – this is the middle point of a range of numbers (if those numbers were arranged from low to high) Standard deviation and variance – these indicate how dispersed a range of numbers are In other words, how close (or far) all the numbers are to (or from) the average • Skewness – this indicates how symmetrical a range of numbers is In other words, they tend cluster into a smooth bell curve shape in the middle (this is called a “normal distribution”), or they skew to the left or right EXAMPLE: In this example, we’re looking at the bodyweight of 10 people In other words, our sample consists of 10 respondents As you can see, these descriptive statistics give us a clear view of the data set • The mean/average weight is 72.4 kilograms • The median is very similar, suggesting that this data set has a relatively symmetrical distribution (i.e a smooth bell curve shape) • The standard deviation of 10.6 indicates that there’s quite a wide spread of numbers (ranging from 55 to 90) • The skewness of -0.2 tells us that the data is slightly negatively skewed Why descriptive statistics matter While these are all fairly basic statistics to calculate (you can calculate all of them in Excel with a few clicks), they’re incredibly important for a few reasons: They help you get both a macro and micro-level view of your data In other words, they help you understand both the big picture and the finer details They help you spot potential errors in the data – for example, if an average is way higher than you’d • intuitively expect, or responses to a question are highly varied They help inform which inferential statistical techniques you can use, as those techniques depend on the skewness (symmetry and normality) of the data Simply put, descriptive statistics are really important, even though the statistical techniques used are fairly basic All too often, we see students skimming over the descriptives in their eagerness to get to the seemingly more exciting inferentials, and then landing up with some very flawed results Don’t be a sucker – give your descriptive statistics the love and attention they deserve Inferential statistics As we discussed earlier, while the 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, inferential statistics aims to make predictions about what you’d find in the full population This could include predictions about: • Differences between groups – for example, height differences between children grouped by their favourite meal • 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 predict what will happen in the real world, based on what you observe in your sample data For this reason, inferential statistics are used for hypothesis testing – in other words, testing statements of change or of difference 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 – i.e you won’t be able to infer very much 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 its 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 post The methods/techniques Some common inferential statistical techniques include: • T-Tests – this compares the averages of two groups of data to assess whether they’re significantly different In other words, they have significantly different means (averages), standard deviations and skewness • ANOVAs – this is similar to a T-test, but it allows you to analyse multiple groups, not just two groups • Correlations – this assesses the relationship between two variables In other words, if one variable goes up, does the other variable also go up, down, or stay the same • Regressions – this is similar to correlation, 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 to move, or they just happen to move together naturally thanks to another force 2 Describe Qualitative data analysis methods Qualitative data refers to non-numeric information such as interview transcripts, notes, video and audio recordings, images and text documents Qualitative data analysis can be divided into the following five categories: Content analysis This refers to the process of categorizing verbal or behavioural data to classify, summarize and tabulate the data Narrative analysis This method involves the reformulation of stories presented by respondents taking into account context of each case and different experiences of each respondent In other words, narrative analysis is the revision of primary qualitative data by researcher Discourse analysis A method of analysis of naturally occurring talk and all types of written text Framework analysis This is more advanced method that consists of several stages such as familiarization, identifying a thematic framework, coding, charting, mapping and interpretation Grounded theory This method of qualitative data analysis starts with an analysis of a single case to formulate a theory Then, additional cases are examined to see if they contribute to the theory Qualitative data analysis can be conducted through the following three steps: Step 1: Developing and Applying Codes Coding can be explained as categorization of data A ‘code’ can be a word or a short phrase that represents a theme or an idea All codes need to be assigned meaningful titles A wide range of non-quantifiable elements such as events, behaviours, activities, meanings etc can be coded There are three types of coding: Open coding The initial organization of raw data to try to make sense of it Axial coding Interconnecting and linking the categories of codes Selective coding Formulating the story through connecting the categories Coding can be done manually or using qualitative data analysis software such as - NVivo, Atlas ti 6.0, HyperRESEARCH 2.8, Max QDA and others When using manual coding you can use folders, filing cabinets, wallets etc to gather together materials that are examples of similar themes or analytic ideas Manual method of coding in qualitative data analysis is rightly considered as labour-intensive, time-consuming and outdated In computer-based coding, on the other hand, physical files and cabinets are replaced with computer based directories and files When choosing software for qualitative data analysis you need to consider a wide range of factors such as the type and amount of data you need to analyse, time required to master the software and cost considerations Moreover, it is important to get confirmation from your dissertation supervisor prior to application of any specific qualitative data analysis software The following table contains examples of research titles, elements to be coded and identification of relevant codes: Research title Elements to be coded Born or bred: revising The Great Man theory of Leadership practice leadership in the 21st century Codes Born leaders Made leaders A study into advantages and disadvantages of various entry strategies to Market entry strategies Chinese market Wholly-owned subsidi Joint-ventures Leadership effectivene Franchising Exporting Licensing Impacts of CSR programs and initiative on brand image: a case study of Philanthropy Supporting charitable Coca-Cola Company UK Activities, phenomenon courses Ethical behaviour Brand awareness Brand value An investigation into the ways of customer relationship management Tactics in mobile marketing environment Viral messages Customer retention Popularity of social networking sites Qualitative data coding Step 2: Identifying themes, patterns and relationships Unlike quantitative methods, in qualitative data analysis there are no universally applicable techniques that can be applied to generate findings Analytical and critical thinking skills of researcher plays significant role in data analysis in qualitative studies Therefore, no qualitative study can be repeated to generate the same results Nevertheless, there is a set of techniques that you can use to identify common themes, patterns and relationships within responses of sample group members in relation to codes that have been specified in the previous stage Specifically, the most popular and effective methods of qualitative data interpretation include the following: • Word and phrase repetitions – scanning primary data for words and phrases most commonly used by respondents, as well as, words and phrases used with unusual emotions; • Primary and secondary data comparisons – comparing the findings of interview/focus group/observation/any other qualitative data collection method with the findings of literature review and discussing differences between them; • Search for missing information – discussions about which aspects of the issue was not mentioned by respondents, although you expected them to be mentioned; • Metaphors and analogues – comparing primary research findings to phenomena from a different area and discussing similarities and differences Step 3: Summarizing the data At this last stage you need to link research findings to hypotheses or research aim and objectives When writing data analysis chapter, you can use noteworthy quotations from the transcript in order to highlight major themes within findings and possible contradictions It is important to note that the process of qualitative data analysis described above is general and different types of qualitative studies may require slightly different methods of data analysis EXAMPLE : - For example, think of a student reading a paragraph from a book during one of the class sessions A teacher who is listening to the reading gives feedback on how the child read that paragraph If the teacher gives feedback based on fluency, intonation, throw of words, clarity in pronunciation without giving a grade to the child, this is considered as an example of qualitative data It’s pretty easy to understand the difference between qualitative and quantitative data Qualitative data does not include numbers in its definition of traits, whereas quantitative data is all about numbers • • The cake is orange, blue, and black in color (qualitative) Females have brown, black, blonde, and red hair (qualitative) ... narrative analysis is the revision of primary qualitative data by researcher Discourse analysis A method of analysis of naturally occurring talk and all types of written text Framework analysis. .. qualitative data analysis starts with an analysis of a single case to formulate a theory Then, additional cases are examined to see if they contribute to the theory Qualitative data analysis can... Qualitative data analysis methods Qualitative data refers to non-numeric information such as interview transcripts, notes, video and audio recordings, images and text documents Qualitative data analysis