(IGNOU) MPC-006 Important Questions with Answers English Medium- MPC-006, Statistics in Psychology, is a core course for the Master of Arts in Psychology (MAPC) program offered by Indira Gandhi National Open University (IGNOU). This course equips students with the fundamental knowledge and skills needed to understand, analyze, and interpret statistical data in the field of psychology.
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- Course Code: MPC-006
- Course Title: Statistics in Psychology
- Credit: 4 credits
- Level: Postgraduate
- Program: MA Psychology
- Duration: 6 months (semester)
Course Structure
Block 1: Introduction to Statistics
- Nature and scope of statistics in psychology
- Data collection methods: observation, surveys, experiments
- Data types: nominal, ordinal, interval, ratio
- Descriptive statistics: measures of central tendency (mean, median, mode), measures of variability (range, variance, standard deviation)
- Frequency distributions and data visualization
Block 2: Correlation and Regression
- Bivariate relationships: scatter plots, correlation coefficient
- Types of correlation: Pearson, Spearman, Kendall
- Linear regression analysis: simple and multiple regression
- Predicting one variable from another
- Assumptions of regression analysis
Block 3: Normal Distribution
- Characteristics of the normal distribution
- Standard normal curve and z-scores
- Applications of normal distribution in hypothesis testing
- Central limit theorem
- Non-parametric alternatives to normal distribution
Block 4: Non-Parametric Statistics
- Chi-square test for independence and goodness-of-fit
- Mann-Whitney U test and Wilcoxon signed-rank test for non-parametric comparison of means
- Kruskal-Wallis test for comparing multiple groups
- Friedman test for comparing multiple related samples
1) Define parametric statistics.
(IGNOU) MPC-006 Important Questions with Answers English Medium- Parametric statistics refers to a branch of statistics that makes certain assumptions about the underlying distribution of the data. These assumptions typically involve the nature of the population distribution, such as assuming normality. In parametric statistics, the data is described using a set of parameters that characterize the population distribution. Common parametric statistical tests include t-tests, analysis of variance (ANOVA), regression analysis, and many others.
- Assumptions: They make specific assumptions about the data, such as normality, linearity, and constant variance.
- Efficiency: When their assumptions hold true, they can be more powerful and efficient than non-parametric methods, requiring smaller sample sizes to achieve the same level of accuracy.
- Common tests: Examples of common parametric tests include t-tests, ANOVA, and linear regression.
- Sensitivity to violations: If the assumptions are violated, the results of parametric tests can be inaccurate and misleading.
MPC-006 Important Questions with Answers – Imagine you want to measure the length of a table. A parametric approach would be like assuming the table is perfectly rectangular and then using a ruler to measure its width and length. You can then calculate the area using the formula for a rectangle. A non-parametric approach would be like simply tracing the outline of the table on a piece of paper and then measuring the area of the cutout. The parametric approach is more efficient and accurate if the table is truly rectangular, but the non-parametric approach is more reliable if the table has an irregular shape. MPC-006 Important Questions with Answers
2) Discuss non-parametric statistics?
3) Write various assumptions of parametric statistics?
4) What are the advantages of non-parametric statistics?
5) Differentiate between parametric and non-parametric statistics?
6) List the assumptions on which the use of Parametric Tests is base.
7) Describe the characteristics of Central Limit Theorem.
8) Define the standard error of mean
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9) What is statistical inference?
10) What are the procedures involved in statistical inference?
11) What is descriptive statistics? Discuss its advantages and disadvantages.
12) What do you mean by organisation of data? State different methods of organising raw data.
13) Define measures of dispersion. Why is it that standard deviation is considered as the best measures of variability?
14) Explain the importance of inferential statistics.
15) Describe the important properties of good estimators.
16) Discuss the different types of hypothesis formulated in hypothesis testing.
17) Discuss the errors involved in hypothesis testing.
18) Explain the various steps involved in hypothesis testing
19) What is Type I error? Give suitable examples.
20) What is Type II error? Give example.