Have you done homework before feeding data into an Excel sheet?
It’s essential for seamless and effective data entry. Otherwise, your data collection won’t pay off what your hard work, indeed, deserves. So! Think for a while before making a final decision on how to collect the data. You should explicitly foresee the methods of data collection you need to opt in.
This blog, particularly, collates the simplest and effective data collection methods for research, be it qualitative or quantitative one. Let’s go through the rundown of these methods:
1. Create a Format: So! Do you have imagery on how you’re going to collect the data? I mean, you should have a specific format to streamline the intended data. Drill into your head that understand-ability should reflect through the data sets. Otherwise, you must have to go back to the drawing board and refine the pan database.
- Categorise the type of data collection, as if it’s descriptive or statistical.
- Select the online tool that can be MS Word or MS excel/MS Access.
- Design a format according to the data type.
2. Transfer Data: Now, you have an explicit picture of what type of data you’re going to collect. If you have a collection of data chunks, put it in the format by creating macros. If you’re not a time-bound, you can manually enter start-to-finish datasets. But, this kind of data entry is more prone to errors. So, get rid of the manual data entries.
- Deploy MS Access/MS Excel for figurative/tabular datasets.
- Feed .doc files with the descriptive data, if any.
- Import data into the sheet/ doc. digitally.
- Easily manipulate electronically, if there is any error.
The puzzles do shoot up, if you haven’t put them in one sheet. Get off multiple sheets. Maintain a single worksheet.
Are you working with the old Excel 2003? The size of those datasets will create a worry.
- Switch off Excel 2003 that carries a limited range of rows (65,536) and columns (256).
- Upgrade to Excel 2007, 2010 or 2013 that has 1,048, 576 rows and 16,384 columns.
4. Unique ID to Label Columns: Unique ID provides a name to the column. It works as an identifier. Your data entry or data researchers collect statistics from different sources. Their data may vary, but you don’t need to tussle hard while undergoing a quality test.
- Provide a unique ID to the columns below column names, like A, B, C and so on.
- Paste or compile from various other sources of your database.
5. Assign Names to Rows: Provide each column with a caption. It denotes what kind of data it consists of. Let’s say, you’ll likely have a format of the mark-sheet. Label its column with the subjects’ name, score and secured marks.
- Label each column with a name that illustrates the type of data it carries.
- Avoid errors by segmenting the exact data under the predefined caption.
6. Fill Up Cells: An empty cell confuses a lot. It may look as if the cell has something missing. Or perhaps, the data were not recorded. It creates a big question during data verification. How can you get it off?
- Mention what if the information is not found, as you can use ‘NOT FOUND’, ‘NO RECORD’ or ‘NO DETAILS’.
- Comment, if you don’t have to specify anything.
7. Separately Enter Codes & Notes: It, often, happens that you create a table with some abbreviations. You should assign a code to differentiate the datasets from different data operators.
- Create a different worksheet of the same excel file to refer the codes & notes.
- Mention how you have measured the things and with which code/ abbreviation.
- Rename the sheet with Notes/Abbreviations/Referral Codes.
8. Consistently Enter Data: Data normalization is all about infusing uniformity to the data in an Excel or a doc file. Each data entry executive has his own preference to denote a few words, like plus, positive, negative and so on. These examples can be written as +,+ve and -ve. It’s what we call inconsistency.
- Use consistent data.
- Predefine what should be abbreviated.
- Train the staff to keep typos similar.
9. Enter Exact or Round Figures: A table can’t be viable or actionable unless it carries the exact statistics. Its comprehensibility would be adversely impacted.
- Avoid assumptions.
- Input the exact numbers.
- You can put the rounded up or rounded down figures.
- Use this function: =Round()
10. Avoid Zero: A zero (0) has multiple illustrations. It can be considered a degree or an assigned value. If you want to connote a meaning, as it should denote -‘no value found’, avoid this practice.
- Use zero for calculations only.
- Don’t use it to let the user get meaning out of it, like Not Found.