Missing values identify
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This document summarizes steps for identifying and treating missing values and outliers in data. It discusses identifying missing values, replacing missing values, analyzing patterns of missing values, imputing missing values using multiple imputation, identifying outliers, and examining data after outlier treatment. The key steps involve analyzing descriptive statistics, frequencies, replacing values, multiple imputation, and identifying outliers in variable distributions and plots.
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instructions and use them quite regularly.
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Basics of SPSS
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T-TEST
Z-TEST
CHI-SQUARE
-GOODNESS OF FIT
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ONE WAY ANOVA
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PEARSON CORRELATION COEFFICIENT
SPEARMAN
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2. For data where whole time series for countries or variables are missing, the "all variable multilevel" method, which uses a multilevel model trained on all available data, works best.
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Missing values identify
- 1. Submitted to: DR. Raza Naqvi Submitted by: M. Irfan Iqbal Class: MBA 1.5 Semester: 1st Subject: Advance research methodology Topic: Identifying and treatment of missing values and outliers Pakistan Institute of Development Economics (PIDE)
- 2. Missing values identify In this step we identify the missing values that are not given in random and non-random data. These are the following steps to identify missing values that are shown in the screen shot. The process of identifying missing values is Analyze > descriptive statics > frequencies >transfer all data to variable side than result comes here frequencies and missing values are shown Replace missing value The next step in replacement of missing values. In which we first note the missing vales and then further treatment for replacement of missing values. This process is also step by step . these steps are given below Transform > replace missing values > than outcome show > than transfer the variable in which values are missing > and then press ok. After treatment of all process new data is shown in which missing values replaced.
- 3. Pattern missing values In this step of pattern missing values we see the diagrams in which missing values are shown in different forms. In this step after treatment we see missing vales shows in tabular form and circle diagram form. Steps are given here for pattern missing values Analyze > multiple imputation > analyze pattern > outcome shows > transfer variables to analyze across variables than results are comes
- 4. Impute missing values In imputing missing values data we treat some variables that are given in the data. This all process is done step by step in which we transfer all variables in variable model side than set the name of data after that we go constraints and then scan the variables data and set its maximum and minimum variables vales and click ok. Analyze > multiple imputation > impute missing value data > transfer data > set the > constraints on top of border > scan data >set maximum minimum variable vales and set rounding > press ok Result of multiple imputation of missing value data
- 5. Outlier identify Outliers are extreme values as compared to rest of the data. This is the process of finding outliers that are shown in the screen shot. These are some steps to identify the outliers. Analyze > descriptive statistics > explore > transfer variables to dependent list> click on statistics and check out on outlier> and then click on plots and un-check the stem and leaf and press continue. Outliers in this diagram
- 6. After treatment of outlier