What is the relationship between quantitative research qualitative research data collection and data analysis?
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 is the relationship between qualitative and quantitative research?In general, quantitative research seeks to understand the causal or correlational relationship between variables through testing hypotheses, whereas qualitative research seeks to understand a phenomenon within a real-world context through the use of interviews and observation.
What is a similarity between quantitative and qualitative methods of data analysis?Are there any similarities between quantitative & qualitative data? Both quantitative and qualitative data has an order or scale to it. That is while ordinal data is sometimes classified under quantitative data. Qualitative data do not, however, have a standardised scale.
What is the difference between quantitative data analysis and qualitative data analysis?Generally speaking, quantitative analysis involves looking at the hard data, the actual numbers. Qualitative analysis is less tangible. It concerns subjective characteristics and opinions – things that cannot be expressed as a number. Here's a closer look at aspects of both and how they are used.
What is data collection and analysis in qualitative research?Qualitative data collection is exploratory; it involves in-depth analysis and research. Qualitative data collection methods are mainly focused on gaining insights, reasoning, and motivations; hence they go deeper in terms of research.
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