SPSS Missing Values

欠損データを診断して、より優れたモデルを構築

アンケート調査や市場調査、社会科学、データ・マイニングなどの多数の専門家が、データ検証に IBM® SPSS® Missing Values を使用しています。

欠損データは、モデルや分析結果に重大な影響を与える可能性があります。欠損データを無視したり、除外しても問題がないとみなすと、不正確で無意味な結果につながる恐れがあります。

  • 6 つの診断レポートの 1 つを使用して、データをさまざまな角度から容易に検証します。その後、要約統計量を推定し、欠損値を補完します。
  • 重大な欠損データの補完問題を素早く診断します。
  • 欠損値を推定値に置き換えます。
  • 各種の欠損値と各ケースの極値のスナップショットを示します。
  • 欠損値を推定値に置き換えることで、データ内の隠れたバイアスを除去し、分析にすべてのグループ (反応性の低いグループも含む) が含まれるようにします。

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Uncover Missing Data Patterns

With IBM SPSS Missing Values, you can easily examine data from several different angles using one of six diagnostic reports to uncover missing data patterns. You can then estimate summary statistics and impute missing values through regression or expectation maximization algorithms (EM algorithms).

IBM SPSS Missing Values helps you to:

  • Diagnose if you have a serious missing data imputation problem

  • Replace missing values with estimates -- for example, impute your missing data with the regression or EM algorithms

Quickly and Easily Diagnose Your Missing Data

Quickly diagnose a serious missing data problem using the data patterns report, which provides a case-by-case overview of your data. This report helps you determine the extent of missing data; it displays a snapshot of each type of missing value and any extreme values for each case.

Reach More Valid Conclusions

Replace missing values with estimates and increase the chance of receiving statistically significant results. Remove hidden bias from your data by replacing missing values with estimates to include all groups in your analysis – even those with poor responsiveness.

Use Multiple Imputation to Replace Missing Data Values

IBM SPSS Missing Values' multiple imputation procedure will help you understand patterns of “missingness” in your dataset and enable you to replace missing values with plausible estimates. It offers a fully automatic imputation mode that chooses the most suitable imputation method based on characteristics of your data, while also allowing you to customize your imputation model.

Several complete datasets are generated (typically, three to five), each with a different set of replacement values. Next, you can model the individual datasets, using techniques such as linear regression, to produce parameter estimates for each dataset. Then you can obtain final parameter estimates. This involves pooling the individual sets of parameter estimates obtained in step two and computing inferential statistics that take into account variation within and between imputations.

Analysis of the individual datasets and pooling of the results are supported via existing IBM SPSS Statistics procedures such as REGRESSION. When operating on datasets with imputed values, existing procedures will automatically produce pooled parameter estimates.

Fill in the Blanks for Improved Data Management


This separate variance t test
table defines two groups of
cases: those with data on
income and those that are
missing data on income.
Then, the separate variance
t test table tests to see if
these two groups are
different from each other on
a series of variables. This
table shows that people with
missing data on income are
more likely to have a non-
professional occupation,
more likely to be female,
more likely to be married,
and have a larger family
than people who reported
information on their family
income.IBM SPSS Missing Values has the statistics you need to fill in missing data:

  • Univariate: compute count, mean, standard deviation, and standard error of mean for all cases excluding those containing missing values, count and percent of missing values, and extreme values for all variables

  • Listwise: compute mean, covariance matrix, and correlation matrix for all quantitative variables for cases excluding missing values

  • Pairwise: compute frequency, mean, variance, covariance matrix, and correlation matrix

  • Expectation maximization (EM) algorithm

  • Estimate the means, covariance matrix, and correlation matrix of quantitative variables with missing values, assuming normal distribution, t distribution with degrees of freedom, or a mixed-normal distribution with any mixture proportion and any standard deviation ratio

  • Impute missing data and save the completed data as a file

  • Regression algorithm

  • Estimate the means, covariance matrix, and correlation matrix of variables set as dependent; set number of predictor variables; set random elements as normal, t, residuals, or none

IBM SPSS Missing Values also has features that enable you to analyze patterns and manage data, including the ability to:

  • Display missing data and extreme cases for all cases and all variables using the data patterns table

  • Determine differences between missing and non-missing groups for a related variable with the separate t test table

  • Assess how much missing data for one variable relates to the missing data of another variable using the percent mismatch of patterns table

We highly recommend using IBM SPSS Missing Values with IBM SPSS Statistics Base.

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SPSS Missing Values