Graphical Recoding Tool

The Graphical Recoding Tool offers a visual and intuitive interface for recoding or re-standardizing spreadsheet data.

If you are involved in data management for statistical purposes, you are prone to stumble upon them sooner or later—the incoherent spreadsheet data. The reasons why data are sometimes fraught with inconsistencies range from random spelling errors to systematic idiosyncrasies because of differences in origin, purpose or language.

Moreover, computer software aimed at re-validating and re-standardizing cloudy data sometimes poses a real challenge to the data manager's organizing skills. It is difficult even to keep in mind such highly abstract programming statements as “RECODE this INTO that REPEAT until LATER”, not to mention typing them correctly into the machine.

Now imagine running a software package that offers a graphical interface for re-validating and re-standardizing data in a visual and intuitive fashion, reducing both the time necessary for a specific assignment and the risk for error at the same time. Interested?

The first step in converting data into a graphical rendition is to enter a dataset. This can be done in several different ways.

If you are involved in data management for statistical purposes, you are prone to stumble upon them sooner or later—the incoherent spreadsheet data. The reasons why data are sometimes fraught with inconsistencies range from random spelling errors to systematic idiosyncrasies because of differences in origin, purpose or language.

Moreover, computer software aimed at re-validating and re-standardizing cloudy data sometimes poses a real challenge to the data manager's organizing skills. It is difficult even to keep in mind such highly abstract programming statements as “RECODE this INTO that REPEAT until LATER”, not to mention typing them correctly into the machine.

Now imagine running a software package that offers a graphical interface for re-validating and re-standardizing data in a visual and intuitive fashion, reducing both the time necessary for a specific assignment and the risk for error at the same time. Interested?

The first step in converting data into a graphical rendition is to enter a dataset. This can be done in several different ways.

If you are involved in data management for statistical purposes, you are prone to stumble upon them sooner or later—the incoherent spreadsheet data. The reasons why data are sometimes fraught with inconsistencies range from random spelling errors to systematic idiosyncrasies because of differences in origin, purpose or language.

Moreover, computer software aimed at re-validating and re-standardizing cloudy data sometimes poses a real challenge to the data manager's organizing skills. It is difficult even to keep in mind such highly abstract programming statements as “RECODE this INTO that REPEAT until LATER”, not to mention typing them correctly into the machine.

Now imagine running a software package that offers a graphical interface for re-validating and re-standardizing data in a visual and intuitive fashion, reducing both the time necessary for a specific assignment and the risk for error at the same time. Interested?

The first step in converting data into a graphical rendition is to enter a dataset. This can be done in several different ways.

If you are involved in data management for statistical purposes, you are prone to stumble upon them sooner or later—the incoherent spreadsheet data. The reasons why data are sometimes fraught with inconsistencies range from random spelling errors to systematic idiosyncrasies because of differences in origin, purpose or language.

Moreover, computer software aimed at re-validating and re-standardizing cloudy data sometimes poses a real challenge to the data manager's organizing skills. It is difficult even to keep in mind such highly abstract programming statements as “RECODE this INTO that REPEAT until LATER”, not to mention typing them correctly into the machine.

Now imagine running a software package that offers a graphical interface for re-validating and re-standardizing data in a visual and intuitive fashion, reducing both the time necessary for a specific assignment and the risk for error at the same time. Interested?

The second step is to create a matrix on which to format a new standard. This can be done in several different ways.

If you are involved in data management for statistical purposes, you are prone to stumble upon them sooner or later—the incoherent spreadsheet data. The reasons why data are sometimes fraught with inconsistencies range from random spelling errors to systematic idiosyncrasies because of differences in origin, purpose or language.

Moreover, computer software aimed at re-validating and re-standardizing cloudy data sometimes poses a real challenge to the data manager's organizing skills. It is difficult even to keep in mind such highly abstract programming statements as “RECODE this INTO that REPEAT until LATER”, not to mention typing them correctly into the machine.

Now imagine running a software package that offers a graphical interface for re-validating and re-standardizing data in a visual and intuitive fashion, reducing both the time necessary for a specific assignment and the risk for error at the same time. Interested?