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SPSS South Asia (P) Ltd

No. 2353/1-4, Hennur Main Road,

KK Halli, Bangalore - 560043

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©2017 by SPSS South Asia Pvt Ltd.

SPSS Missing Value Analysis allows you to quickly and easily diagnose your missing data and fill in the blanks to create higher-value data which result in better models. When you ignore or exclude missing data, you risk reaching invalid and insignificant results.

Even in the best-designed and monitored study or survey, observations can be missing—a person
inadvertently skips a question, a sample or response is illegible or there are technical malfunctions. Don’t risk invalid results! With SPSS Missing Value Analysis, you can:

 

  • Diagnose if you have a serious missing data problem.

  • Replace missing values with estimates.

  • Ensure you enter the data analysis stage using data that takes missing values into account.

  • Improve survey questions—identify possibly troublesome or confusing questions, based on observed missing data patterns.

  • Draw more valid conclusions and remove hidden bias from your data.

Draw more valid conclusions with SPSS Missing Value Analysis

It’s easy for you to evaluate the effect of missing data, especially with small datasets, because SPSS Missing Value Analysis offers the features and benefits below.

Six tailor-made displays: Examine data from several angles using six diagnostic reports to uncover missing data patterns.

Diagnose missing data problems: Get a case-by-case overview of your data with the data patterns report. By giving you a snapshot of each type of missing value and any extreme values for each case, you can determine the extent of missing data quickly.

Better summary statistics: Adjust for missing values so you get a more accurate description of your data. Choose from four methods: listwise
deletion, pairwise deletion, EM, and covariance matrix.

Fill in missing data: Easily replace missing values with estimates and increase your chance of reaching statistically significant results. Choose
from the EM and regression algorithms to predict missing values based on data you already have.