Best SPSS Training ▷ 100% Job ▷ Data Analytics Course ▷ SLA Institute, Laxmi Nagar, Delhi

Basic & Advanced SPSS Certification

Factor Analysis

Profile Analysis

Survival Analysis

Neural network Analysis

Time Series Analysis

Data Analysis Using SPSS Course

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Take a Look to Our Data Analysis Using SPSS Training Course

Certification

Industry-oriented certification Tableau training course to furnish your career to kick-start/grow at MNCs/Non-MNCs Organization and corporate.

Experienced Faculty

Professional expert Sr. Lead Analyst / Technical Analyst With 10+ Years of working experience will provide you industrial level training.

Placement Assistance

Receive placement assistance after completion of 70% training, SLA team will arrange interviews till you’ll get the placement.

LAB Facility

You can attain the practical training session to get the indutrial exposure and prepare for the corporate level challanges.

Workshop Sessions

You’ll attain knowledge on bases of real-time scenarios, live projects and best case study to complete SLA workshop.

Admin Support

SLA’s 7 days working admin team co-ordinates you for the fresh batch schedule/re-scheduling classes/arrange doubt classes.

Data Analysis Using SPSS Programme Details

Course Duration: 55 HRS with Highly Skilled Corporate Trainers

Data Analysis using SPSS Training for 2 Modules-Weekdays/Weekend

SLA Consultants Delhi-Introduce SPSS Training Course Delivery Method:

Web / Desktop Base.
Instructor-Led Classroom Sessions.
Presentations/Demonstration/Practicals of concepts.
Handouts/Assignments/Real-time Exposure to work on Clients.

NOTE: Classes Are Also Available On Module Basis

Data Analysis Using SPSS Programme Modules

Data Analysis Using SPSS Training Modules

Module 1.1 – Developing the familiarity with SPSS Processer

Entering data in SPSS editor
Solving the compatibility issues with different types of file
Inserting and defining variables and cases
Managing fonts and labels
Data screening and cleaning
Missing Value Analysis
Sorting
Transposing
Restructuring
Splitting and Merging
Compute and Recode functions.
Visual Binning and Optimal Binning
Research with SPSS (random number generation)

Module 1.2 – Working with descriptive statistics

Frequency tables
Using frequency tables for analyzing qualitative data Explore
Graphical representation of statistical data
histogram (simple vs. clustered)
boxplot
line charts
scatterplot (simple, grouped, matrix, drop-line)
P-P plots, Q-Q plots
Addressing conditionality’s and errors
Computing standard scores using SPSS
Reporting the descriptive output in APA

Module 1.3 – Hypothesis Testing

Sample and Population, concept of confidence interval
Testing normality assumption in SPSS
Testing for Skewness and Kurtosis
Kolmogorov–Smirnov test
Test for outliers Mahalanobis Test
Dealing with the non-normal data
testing for homoscedasticity (Levene’s test) and multicollinearity.

Module 1.4 – Testing the differences between group means

t – test (one sample, independent – sample, paired sample)
ANOVA-GLM 1 (one way)
Post-hoc analysis, reporting the output in APA format.

Module 1.5 – Correlational Analysis

Data entry for correlational analysis
Choice of a suitable correlational coefficient
non-parametric correlation (Kendall’s tau)
Parametric correlation (Pearson’s, Spearman’s)
Special correlation (Biserial, Point-biserial)
Partial and Distance Correlation

Module 1.6 – Regression

The method of Least Squares
Linear modeling
Assessing the goodness of fit
Simple regression
Multiple regression (sum of squares, R and R2 , hierarchical, step-wise)
Choosing a method based on your research objectives
checking the accuracy of regression model. Logistic regression
Reporting the output in APA format.

Module 1.7 – Non-parametric tests

When to use
Assumptions
Comparing two independent conditions (Wilcoxon rank-sum test, Mann- Whitney test)
Several independent groups (Kruskal- Wallis test)
Comparing two related conditions (Wilcoxon signed-rank test)
Several related groups (Friedman’s anova)
Post-hoc analysis in non- parametric analysis
Categorical testing: Pearson’s Chi-square test
Fisher’s exact test
Likelihood ratio
Yates’ correction
Log linear Analysis
Reporting the output in APA format.

Module 2.1 – General Linear Models (GLM 1 to 5)

Theoretical basis of GLM: Assumptions and practical considerations
Comparing several means
ANCOVA
Factorial Anova
Repeated Measure Anova
Mixed Design Anova
MANOVA

Module 2.2 – Factor Analysis

Theoretical foundations of factor analysis
Exploratory and Confirmatory factor analysis
testing data sufficiency for EFA and CFA
Principal component Analysis
Factor rotation
factor extraction
using factor analysis for test construction
Interpreting the SPSS output: KMO and Bartlett’s test
initial solutions
correlation matrix
anti-image
explaining the total variance
communalities
Eigen-values
scree plot
rotated component matrix
component transformation matrix
factor naming

Module 2.3 – Cluster Analysis

Basic concepts
purpose and uses
selecting distance measures
K-Means clustering and hierarchical clustering
combining the clusters
working with the SPSS output: agglomeration schedule
proximity matrix
cluster membership
icicle plot and dendograms
Transform values and transform measures

Module 2.4 – Profile Analysis

Basic concepts
Assumptions and Practical issues
Difference in levels
Parallelism and flatness in profiles
profile contrast (simple effect analysis and interaction contrast)
Doubly-Multivariate Design
Classifying profiles
Practical Demonstration using standard scales
Interpreting the SPSS output: test of between subject effects (Intercepts, groups, effects), test of within subject effects-sphericity
Greenhouse-Geisser, Hyunh-Feldt
test of within subject contrast- linear
quadratic
cubic

Module 2.5 – Discriminant Analysis

Derivation and test of discriminate function
types of discriminant function: direct
sequential
stepwise
Interpreting discriminant function: discriminant plots
structure matrix loading
Interpreting the SPSS output: Log determinants
Box test
Walks’ Lambda
canonical discriminant function coefficient
Function at group centroids, Classification results

Module 2.6 – Survival Analysis

Meaning and types: non-parametric
semi-parametric and parametric
Life tables
cumulative proportion surviving
Hazard and density functions
table of group differences
Using SPSS: procedures-Kaplan-Meier
Cox-Regression
Cox-Regression with time dependent covariate
survival function
Omnibus Test

Module 2.7 – Neural network Analysis

Understanding neural network structures
multilayer perceptron’s-uses in estimation of cost and time (predicted-by-observed charts
Residual-by-predicted charts)
Radial basis functions-ROC curve
cumulative gains and lift charts

Module 2.8 – Time Series Analysis

Using time series analysis for forecasting
detecting trends and patterns in data
Box-Jenkin (ARIMA) method – forecasting using moving averages method
forecasting using trend analysis
seasonal decomposition
spectral plots
running analysis
understanding and interpreting output

Find the course details below. Classes and Course can be designed/customize as per request

Duration of Module

Data Analysis Course Module	Course Duration
SPSS Basic	25 Hours
SPSS (Basic + Advanced)	55 Hours

Batch Timings

MIS Analyst Schedule	MIS Analyst Timing
Monday to Friday	8:00 AM – 10:00 AM
Monday to Friday	10:00 AM – 12:00 PM
Monday to Friday	2:00 PM – 4:00 PM
Monday to Friday	4:00 PM – 6:00 PM
Saturday/Sunday	8:00 \| 12:00 \| 2:00 \| 4:00
Sunday	8:00 AM – 10:00 AM
Sunday	10:00 AM – 12:00 PM
Sunday	2:00 PM – 4:00 PM
Sunday	4:00 PM – 6:00 PM

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