SPSS is statistics and data analysis program for businesses, governments, research institutes, and academic organizations. This course takes highly practical and visual approach to SPSS. From importing Spreadsheets to creating regression models, to exporting charts, this program covers all of the fundamentals with an emphasis on clarity, interpretation, communicability, and application.
Training on SPSS Statistical Software in Kuala Lumpur, Malaysia will be highly beneficial for any professional who applies SPSS statistical software in daily work life. It is equally useful for students, graduates and postgraduates who need to process and analyze data for their research and studies through SPSS. Some of the target attendees are listed below:
- Academic Scholars and Teachers
- Graduates, Master and Doctoral Level Students
- Market Researchers
- Independent Researchers
- Corporate and Governmental Professionals.
Upon the successful completion of this training program, the participants will develop an ability to independently analyze and treat data, plan and carry out research work based on their interest. The course encompasses the majority of research techniques employed in academic and professional studies.
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 & Recode functions. Visual Binning & Optimal Binning. Research with SPSS (random number generation).
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 conditionalities and errors, computing standard scores using SPSS, reporting the descriptive output in APA format.
Sample & 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.
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.
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.
Regression (Linear & Multiple)
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.
Choosing method (Enter, forward, backward) & covariates, choosing contrast and reference (indicator, Helmert and others), predicted values: probabilities & group membership, Influence statistics: Cook, Leverage values, DfBetas, Residuals (unstandardized, logit, studentized, standardized, devaince), Statics and plot: classification, Hosmer-Lemeshow goodness-of-fit, performing bootstrap, Choosing the right block, interpreting -2loglikelihood, Omnibus test, interpreting contingence and classification table, interpreting Wald statistics and odd ratios. Reporting the output in APA format.
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, Loglinear Analysis. Reporting the output in APA format.
Theoretical foundations of factor analysis, Exploratory and Confirmatory factor analysis, testing data sufficiency for EFA & CFA, Principal component Analysis, Factor rotation, factor extraction, using factor analysis for test construction, Interpreting the SPSS output: KMO & 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.
This is highly interactive and practice-oriented training course. All sessions will include the discussion of theoretical concepts followed by practical SPSS demonstration on real data. Participants are welcome to bring and discuss their actual problems related to quantitative analysis.
Program Name: Training on SPSS Statistical Software in Kuala Lumpur, Malaysia
Program Dates: 08 – 12 February 2021
Registration Closes on: 31 December 2020
Venue: Pacific Regency Hotel Suites
Program Fee: $2950
Fee Covers: Visa Assistance (complementary), Participant Assessment, Airport Pickup, Accommodation, Wi-Fi, Breakfast, Workshop Kit, Interactive workshop, Program Materials, Local Expert, Practical Activities, Lunch, Refreshments, City Map, Field Visit, Certificate & Entertaining Tour.Register Online
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