Use the Statistical Data Conversion app to convert a data file from one supported format to another. The maximum upload size is 20 MB. The app identifies the input format from the file extension.
| Format | Input | Output |
|---|---|---|
| SPSS | .sav | .sav |
| SAS data | .sas7bdat | .sas7bdat |
| SAS transport | .xpt | .xpt |
| Stata | .dta | .dta |
| Excel | .xls, .xlsx | .xlsx |
| Comma-separated values | .csv | .csv |
| Text data | .txt, .dat | .txt |
The app exports Excel files as .xlsx; it does not export .xls. Text output is tab-delimited.
| Option | Description |
|---|---|
| Variable names in first row | Choose Yes if the first row of CSV, TXT, or DAT input contains column names. Choose No if it contains data; the app will assign names such as V1 and V2. |
| Value labels | For CSV, TXT, or Excel output, choose Keep numeric codes to retain the underlying values, or Replace codes with labels to write available value labels as text. This setting does not affect other output formats. |
SAS data files (.sas7bdat): These files are created with write_sas(). The SAS file format is complex and undocumented, so this output is unreliable; in most cases, SAS will not read the file.
For reliable SAS compatibility, choose SAS transport (.xpt). write_xpt() produces files in the open SAS transport format, which SAS reliably reads. The transport format has limitations.
.sas7bdat file, convert the data to .xpt instead.WebPower has exceeded 100,000 power analyses today. Thank you for using WebPower.
To try it out: http://webpower.psychstat.org/models/diagram/
A brief intro is here: http://webpower.psychstat.org/wiki/manual/diagram_based_power_analysis
Statistical power analysis is an important topic in research designs. Power analysis methods for a variety of models, such as multiple regression and ANOVA, have been well developed and implemented in popular statistical software (e.g., SAS PROC POWER). The implementation of power analysis for multilevel models, however, is less common. As part of the Webpower project, we developed online software that allows researchers to conduct power analysis for popular multilevel models.
In this week’s QSG, I am going to (1) introduce widely used multilevel designs, including cluster randomized trials and multisite randomized trials; (2) introduce the procedure of power analysis for these multilevel models; and (3) illustrate the application of Webpower.
The goal of power analysis is to decide (a) how large a sample is needed to enable statistical judgments that are accurate and reliable, and (b) how likely your statistical test will be able to detect the effect of a given size in a particular situation.
In the literature, there is no consensus on the approach to compute the power and sample size with logistic regression. In this talk, I am going to review three test statistics used in the asymptotic way. For the implementation, I follow the procedure advocated by Demidenko(2007). As part of the WebPower project, online software is developed and will be demonstrated at the end of the talk.
For more information about WebPower, please visit http://webpower.psychstat.org/wiki/.