# IGNOU BPCC 108 Important Questions With Answers English Medium

IGNOU BPCC 108 Important Questions With Answers English Medium-BPCC 108, offered by Indira Gandhi National Open University (IGNOU), takes you deeper into the realm of statistical analysis in psychological research.

Course Structure:

• Block 1: Inferential Statistics - An Introduction: This introductory block revisits core concepts of inferential statistics from BPCC-104, focusing on hypothesis testing, significance levels, and Type I/II errors. It also introduces advanced topics like power analysis and effect size interpretation.
• Block 2: Parametric Statistics: This section delves into advanced parametric tests used for analyzing data that meet specific assumptions like normality and homogeneity of variance. Students learn about ANOVA, multiple comparisons, and other techniques for analyzing group differences and relationships between variables.
• Block 3: Non-Parametric Statistics: This block focuses on non-parametric tests, which are used when data violates assumptions of parametric tests. Students learn about chi-square tests, Mann-Whitney U test, Wilcoxon signed-rank test, and other techniques for analyzing ordinal and non-normal data.
• Block 4: Statistical Package for Social Sciences (SPSS): This hands-on section equips students with the skills to use SPSS, a widely used statistical software package. Students learn how to enter data, conduct analyses using various techniques, interpret results, and create data visualizations.
• Block 5: Recent Developments in Statistical Methods: This final block exposes students to emerging trends and recent advancements in statistical methods relevant to psychological research. It may cover topics like structural equation modeling (SEM), multilevel modeling, and Bayesian statistics.

Q.1 Discuss the concepts of parametric and non-parametric statistics.

Parametric and non-parametric statistics are two fundamental approaches employed in statistical analysis to derive conclusions and infer properties about populations using sample data. These methodologies diverge in their underlying assumptions, methodologies, and utility, providing researchers with versatile tools for data analysis across various contexts.

Parametric statistics rely on specific assumptions concerning the distribution of the population from which the sample is drawn. These assumptions typically pertain to the shape and parameters of the population distribution, such as normality and homogeneity of variance. When these assumptions are satisfied, parametric methods offer several advantages, including heightened statistical power and efficiency.

IGNOU BPCC 108 Important Questions With Answers English Medium-One notable advantage of parametric statistics is their capability to accurately estimate population parameters using sample data. For instance, widely used parametric tests like t-tests and analysis of variance (ANOVA) are effective for comparing means across different groups or conditions. Moreover, parametric techniques often yield more precise estimates and narrower confidence intervals compared to non-parametric approaches.

However, parametric statistics necessitate strict adherence to assumptions, and deviations from these assumptions can lead to biased outcomes and erroneous conclusions. If the data fail to adhere to a normal distribution or exhibit unequal variances between groups, parametric tests may yield inaccurate results. Furthermore, parametric tests might be sensitive to outliers and less robust when dealing with small sample sizes. Despite these limitations, parametric methods remain valuable tools in numerous research scenarios, particularly when assumptions are met and sample sizes are adequate to support their application.

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IGNOU BPCC 108 Important Questions With Answers English Medium-In contrast, non-parametric statistics operate independently of assumptions about the underlying population distribution. Instead, non-parametric methods are distribution-free and rely on ranks or frequencies of observations within the sample. These techniques prove especially valuable when the data do not align with the assumptions of parametric tests or when dealing with limited sample sizes.

Non-parametric methods offer several advantages, including robustness to outliers and deviations from distributional assumptions, rendering them suitable for analyzing skewed or non-normally distributed data. Additionally, non-parametric tests are often more adaptable and can be applied to various data types, including ordinal and nominal data.

Non-parametric statistics encompass a range of techniques, including rank-based tests like the Mann-Whitney U test and the Kruskal-Wallis test, which compare medians across groups based on ranks rather than raw values. These tests exhibit reduced sensitivity to outliers and distributional assumptions. Non-parametric methods also include tests for independence, such as the chi-square test and Fisher's exact test, which evaluate the association between categorical variables without presupposing a specific distribution.

IGNOU BPCC 108 Important Questions With Answers English Medium-Despite their advantages, non-parametric methods may exhibit lower statistical power compared to parametric tests, particularly when the assumptions of parametric methods hold true. Additionally, non-parametric tests might yield less precise estimates and wider confidence intervals, limiting their efficacy in certain contexts. Moreover, some non-parametric tests may necessitate careful interpretation of effect sizes and practical significance, making them potentially less intuitive than their parametric counterparts.

Q.2 Explain the assumptions of parametric statistics.

Q.3 Differentiate between parametric and non-parametric statistics

Q.4 Explain the concept of Standard error and level of significance.

Q.5 Explain Kruskal–Wallis ANOVA test.

Q.6 Explain the procedure for computation of correlation in SPSS.

Q.7 State the procedure for computation of t test in SPSS.

Q.8 What is the procedure for computation of One-Way ANOVA.

Q.9 How is the computation of t test different from computation of Mann Whitney U test in SPSS.

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Q.10 Describe how to compute frequency using SPSS

Q.11 Discuss the procedure involved in descriptive statistics.

Q.12 Explain what is crosstabs and discuss the procedure to create crosstabs using SPSS.

Q.13 Explain the procedure to draw graphs in SPSS.

Q.14 Explain the concept of Standard error and level of significance.

Q.15 What do you understand by the goodness of fit test ? Describe with examples