Top 5 techniques for Data Collection and Tools For Structuring Data

Techniques for Data Collection

Hello friends. We had seen various tools and techniques about how to read the data and analyze it. Every tool will be coming in the picture once the data collection is complete. The power of these tools is effective only when the collection of data is effective. so, in this article, I’m going to explain very simple, but the most important top 5 techniques for data collection and tools for structuring data. without which you cannot analyze the data. This effective data collection not only reduces the work but also time to achieve sustainable results. At this point, we know the types of data and applicable tools in a particular type of data. So let’s begin the learning of these techniques.

Data collection techniques:

The information you gather can come from a range of sources. Likewise, there are a variety of techniques use when gathering primary data. Listed Below are some of the most common data collection techniques.

  1. Interviews: In-depth interviews include both individual interviews, for example, one-on-one as well as group interviews. The data can record in a wide variety of ways including stenography, audio recording, video recording or written notes. In interviews, assume that there are a questioner and one or more interviews. The purpose of the interview is to probe the ideas of the interviews about the phenomenon of interest.
  2. Surveys or questionnaires: Surveys or questionnaires use for collecting data. In Survey Research. They usually include a set of standardized questions that explore a specific topic and collect information about demographics, opinions, attitudes or behaviors.
  3. Observations: This is going directly or indirectly with a subject knowing or unaware that you are observing them. You may choose to collect data through continuous observation or via saving periods depending on your project.
  4. Case studies: A case study is usually an in-depth description of a process, experience or structure at a single institution to insert a combination of what and why Questions. Case study generally involves a mix of quantitative. For example, surveys, usage statistic, etc, and qualitative. For example, interviews, focus groups, etc. Data collection techniques. Most often the researcher will analyze quantitative data first and then use qualitative strategies to look deeper into the meaning of the trends. Identifying the numerical data.
  5. Checklist: Here you stop criteria that can be Mark as present or absent can provide space for observer comments. These tools can provide consistency over time or between observers. The checklist is used for evaluating databases or structuring Peer Observations of instruction sessions.

The collected data from the above techniques need to be structured so that the analysis of the data will be very easy with simple analysis tools. I’m going to explain two such tools used to structure data. Let’s see one by one.

1. Stratification:

stratification is a technique used in combination with other data analysis tools. When data from a variety of sources or categories group together. The meaning of the data can be impossible to see. These techniques separate the data so that patterns can visible.

When do you stratification:

  1. Before collecting data
  2. When data came from several sources or conditions such as shifts, days of the week, suppliers, and our population group
  3. When data analysis, May require separating different sources or conditions.

Stratification Procedure:

1. Before collecting data, consider which information about the sources of data might affect the results. Set up the data collection so that you collect that information as well.

2. When plotting or graphing. The collected data on a scatter diagram, control chart, histogram or another analysis tool. Use different marks or different colors to distinguish data from various sources. Data that distinguish in this way have to stratify.

3. Analyze the subset of stratified data. For example, on a scatter diagram where the data is stratified into data from source one and data from source two. Drop ordinance count points and determine the critical value only for the data from source one, and then only for the data from source two stratification. Example, a manufacturing team do us a scatter diagram to test whether product purity and ion contamination relate to each other, but the plot did not show a relationship.

Then Team member realize that the data came from three different directors. The team member, redo the diagram using the different symbol for each reactor Data. Now pattern can visible. The data from reacted to and reacted three as a circle. Even without doing any calculation, it is clear that for those two reactors, purity decreases as ion increases. Yet, the data from reactor one, the solid doors that have not circled do not show that relationship. Something is different about reactor one.

Stratification considerations:

Here are some of the examples of different sources that might need data to stratify like equipment, sheets, departments, materials, suppliers, day of the week, time of the day, or products. Survey data usually enjoy stratification and what’s considered before collecting data, whether stratification might need during analysis. Plan to collect stratification information after the data collected. It might be too late on your graph or chart included a region that identifies the marks or colors used.

2. Check sheet:

It is also called a defect concentration diagram. It checks the structure repaired form for collecting and analyzing data. This is a generic tool that can adapt to a wide variety of purposes. Now let’s see when to use a check sheet. When data can absorb and collected by the same person or at the same location. When collecting data on a frequency or patterns or prevents problems, defects, defect, location, defect causes, etc. And when collecting data from the production process.

Procedure to create a check sheet:

  1. Decide what event or problem will be observed. Develop operational definitions for that.
  2. Decide when data will be collected and for how long.
  3. Designed to form, set it up so that data can be recorded simply by making checkmarks or similar symbols and so that data do not have to be recopied for analysis
  4. Label all spaces on the form
  5. Test the check sheet for a short period to be sure it collects that appropriate data and it’s very easy to use.
  6. Each time the targeted event or problem occurs record data on the check sheet

Share this article in your entire group. You can put their life at a provisional level so that they can start using these useful techniques to have a small improvement in industry, state, country, and thereby world. And finally, thank you for reading.

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