**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.

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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.

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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**

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