To make Excel check data entry for invalid entries, follow these steps:

1. Data Entry Error Checking software, free download 64-bit
2. Manual Data Entry Error Rate
3. Data Entry Error Rate Standards
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1. Select the cell or range you want Excel to check.

2. On the Data tab, in the Data Tools group, clickData Validation:

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3. In the Data Validation dialog box, on the Settingstab, specify the validation criteria to use:

Select the appropriate type in the Allow drop-down list, and then set parameters accordingly:

• Any Value accepts any input (Excel's default setting for cells). This setting effectivelyturns off validation, so you usually select it only when you need to remove validation from a cellor range. But you can also use this setting to display an informational message for a cell or range.To do so, enter the title and message on the Input Message tab, as discussed in step 4.
• Whole Number lets you specify a comparison operator and appropriate values. The user must notenter a decimal point.

  The validation criteria use these self-explanatory comparison operators: Between,Not Between, Equal To, Not Equal To,Greater Than, Less Than, Greater Than orEqual To, and Less Than or Equal To.

• Decimal lets you specify a comparison operator and appropriate values. The user must includea decimal point and at least one decimal place (even if it's .0).
• List lets you specify a list of valid entries for the cell. You can type in entries in theSource text box, separating them with commas, but the best form of the source is a range on aworksheet in this workbook. If you hide the worksheet, the users won't trip over it. Usually, you'llwant to select the In-Cell Dropdown option to produce a drop-down list in the cell.Otherwise, users have to know the entries (or enter them from the help message).
• Date lets you specify a comparison operator and appropriate dates (including formulas).
• Time lets you specify a comparison operator and appropriate times (including formulas).
• Text Length Lets you specify a comparison operator and appropriate values (includingformulas).
• Custom lets you specify a formula that returns a logical TRUE or a logicalFALSE value.

4. On the Input Message tab, choose whether to have Exceldisplay an input message when the cell is selected. If you leave the Show Input Message When Cell IsSelected checkbox selected (as it is by default), enter the title and input message in the textboxes:

When a user selects a restricted cell, Excel displays the information message (unless you chose not todisplay one), E.g.:

5. On the Error Alert tab, choose whether to have Excel displayan error alert after the user enters invalid data in the cell. If you leave the Show Error AlertAfter Invalid Data Is Entered checkbox selected (as it is by default), choose the style(Stop, Warning, or Information) in the Styledrop-down list, and enter the title and error message in the text boxes.

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• Stop alerts prevent the user from continuing until they enter a valid value for the cell.
• Warning alerts and Information alerts display the message but allow the user tocontinue after entering an invalid value in the cell.

6. Click the OK button to close the Data Validationdialog box and apply the validation to the cell or range.

Manual Data Entry Error Rate

See also this tip in French:Comment vérifier l'entrée de données pour les valeurs non valides.

Errors are the norm, not the exception, when working with data. By now, you’ve probably heard the statistic that 88% of spreadsheets contain errors. Since we cannot safely assume that any of the data we work with is error-free, our mission should be to find and tackle errors in the most efficient way possible. Everyone has their own way of catching errors, but here are a few quick strategies that have helped me over the years:

Gauge min and max values

For continuous variables, checking the minimum and maximum values for each column can give you a quick idea of whether your values are falling within the correct range. For example, with a variable like age, I want to make sure that my minimum value makes sense, and that I’m not maxing out somewhere in the 400’s. Checking min and max values is a great way to spot extra zeroes or missing digits, and fix them before they enter an analysis.

Look for missings

The easiest way to find missings is to perform a count, if you have this function available. If not, there are other ways to find missing values. Try sorting your columns (both ‘ascending’ and ‘descending’) to see if any missing values exist in your columns, or filtering your dataset such that you’re only looking at records with a missing value. While sometimes missing values are inevitably due to chance, it’s worth double-checking to see if there might be an underlying reason for missingness, and address them as best you can.

Check the values of categorical variables

Depending on your methodology and the number of people contributing to a database, there can be lots of room for error when entering data. One quick way to find these is to pull up all of the different categories that a categorical variable can take on. A quick example: recently I was looking at a field set to store U.S. states. The values in the upper part of the spreadsheet looked good – ‘NH’, ‘ME’, etc. – but when I got to the lower part, the entry method had switched to more full values – ‘New Hampshire’ and ‘Maine’. Correcting the categories was a quick fix, but it’s best to catch these errors as early on as possible.

Look at the ‘incidence rate’ of binary variables

Data Entry Error Rate Standards

If we think of a true binary variable as one made up of 1’s and 0’s, looking at its mean (or the incidence rate) will tell you the proportion of 1’s you have in your dataset. It’s worth double-checking this to make sure that your binary is set up correctly. One common mistake I’ve seen is to have 1’s and nulls, rather than 1’s and 0’s. This becomes easy to spot because the “rate” of the binary variable will be equal to 1. The proportion of 1’s you get should make sense for the behavior you are trying to flag within your dataset.

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Catching errors early on in the process is important so that early mistakes don’t influence decisions later on in the analysis. After all, our analysis is only as good as the data we use to create it. These are some of my most efficient mistake-catching techniques, but I’m sure there are others. So I’d love to know -what are your best strategies for finding errors in your data?