What is the first thing that comes to mind when we see data? The first instinct is to find patterns, connections, and relationships. We look at the data to find meaning in it. Show
Similarly, in research, once data is collected, the next step is to get insights from it. For example, if a clothing brand is trying to identify the latest trends among young women, the brand will first reach out to young women and ask them questions relevant to the research objective. After collecting this information, the brand will analyze that data to identify patterns — for example, it may discover that most young women would like to see more variety of jeans. Data analysis is how researchers go from a mass of data to meaningful insights. There are many different data analysis methods, depending on the type of research. Here are a few methods you can use to analyze quantitative and qualitative data. It’s difficult to analyze bad data. Make sure you’re collecting high-quality data with our blog “4 Data Collection Techniques: Which One’s Right for You?”. Analyzing Quantitative DataData PreparationThe first stage of analyzing data is data preparation, where the aim is to convert raw data into something meaningful and readable. It includes four steps: Step 1: Data ValidationThe purpose of data validation is to find out, as far as possible, whether the data collection was done as per the pre-set standards and without any bias. It is a four-step process, which includes…
To do this, researchers would need to pick a random sample of completed surveys and validate the collected data. (Note that this can be time-consuming for surveys with lots of responses.) For example, imagine a survey with 200 respondents split into 2 cities. The researcher can pick a sample of 20 random respondents from each city. After this, the researcher can reach out to them through email or phone and check their responses to a certain set of questions. Check out 18 data validations that will prevent bad data from slipping into your data set in the first place. Step 2: Data EditingTypically, large data sets include errors. For example, respondents may fill fields incorrectly or skip them accidentally. To make sure that there are no such errors, the researcher should conduct basic data checks, check for outliers, and edit the raw research data to identify and clear out any data points that may hamper the accuracy of the results. For example, an error could be fields that were left empty by respondents. While editing the data, it is important to make sure to remove or fill all the empty fields. (Here are 4 methods to deal with missing data.) Step 3: Data CodingThis is one of the most important steps in data preparation. It refers to grouping and assigning values to responses from the survey. For example, if a researcher has interviewed 1,000 people and now wants to find the average age of the respondents, the researcher will create age buckets and categorize the age of each of the respondent as per these codes. (For example, respondents between 13-15 years old would have their age coded as 0, 16-18 as 1, 18-20 as 2, etc.) Then during analysis, the researcher can deal with simplified age brackets, rather than a massive range of individual ages. Quantitative Data Analysis MethodsAfter these steps, the data is ready for analysis. The two most commonly used quantitative data analysis methods are descriptive statistics and inferential statistics. Descriptive StatisticsTypically descriptive statistics (also known as descriptive analysis) is the first level of analysis. It helps researchers summarize the data and find patterns. A few commonly used descriptive statistics are:
Descriptive statistics provide absolute numbers. However, they do not explain the rationale or reasoning behind those numbers. Before applying descriptive statistics, it’s important to think about which one is best suited for your research question and what you want to show. For example, a percentage is a good way to show the gender distribution of respondents. Descriptive statistics are most helpful when the research is limited to the sample and does not need to be generalized to a larger population. For example, if you are comparing the percentage of children vaccinated in two different villages, then descriptive statistics is enough. Since descriptive analysis is mostly used for analyzing single variable, it is often called univariate analysis. Analyzing Qualitative DataQualitative data analysis works a little differently from quantitative data, primarily because qualitative data is made up of words, observations, images, and even symbols. Deriving absolute meaning from such data is nearly impossible; hence, it is mostly used for exploratory research. While in quantitative research there is a clear distinction between the data preparation and data analysis stage, analysis for qualitative research often begins as soon as the data is available. Data Preparation and Basic Data AnalysisAnalysis and preparation happen in parallel and include the following steps:
Qualitative Data Analysis MethodsSeveral methods are available to analyze qualitative data. The most commonly used data analysis methods are:
These methods are the ones used most commonly. However, other data analysis methods, such as conversational analysis, are also available. Data analysis is perhaps the most important component of research. Weak analysis produces inaccurate results that not only hamper the authenticity of the research but also make the findings unusable. It’s imperative to choose your data analysis methods carefully to ensure that your findings are insightful and actionable. What are the steps of qualitative data analysis?Qualitative data analysis requires a 5-step process:. Prepare and organize your data. Print out your transcripts, gather your notes, documents, or other materials. ... . Review and explore the data. ... . Create initial codes. ... . Review those codes and revise or combine into themes. ... . Present themes in a cohesive manner.. What is the first step in analyzing quantitative data?Looking at descriptive statistics is the first step in analyzing any quantitative data on target population, program participation, and outcomes.
What is the first step in qualitative data analysis after transcribing?. Representation of audible and visual data into written form is an interpretive process which is therefore the first step in analysing data. Different levels of detail and different representations of data will be required for projects with differing aims and methodological approaches.
What are the 5 methods to analyze qualitative data?5 qualitative data analysis methods explained. Content analysis.. Thematic analysis.. Narrative analysis.. Grounded theory analysis.. Discourse analysis.. |