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MPC-006: Statistics in Psychology is a Theory subject in IGNOU. This course is taught in the following programme(s) of Indira Gandhi National Open University: Master of Arts (Psychology). This subject is part of the following programme code(s) in IGNOU: MAPC. Continue browsing to get solved assignments, handwritten assignments, question papers, exam help, guide books, and help books for MPC-006.

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MPC 006 Solved Assignment – STATISTICS IN PSYCHOLOGY

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IGNOU MPC 006 SOLVED ASSIGNMENT 2023-24

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• Post published: January 2, 2024
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IGNOU MPC 001 SOLVED ASSIGNMENT 2023-24

Cognitive Psychology, Learning and Memory (MPC-001) TUTOR MARKED ASSIGNMENT (TMA) Course Code: MPC-001 Assignment Code: MPC-001/ASST/TMA/2023-24 Marks: 100 NOTE: All questions are compulsory.

SECTION – A Answer the following questions in 1000 words each. 3 x 15 = 45 marks 1. Discuss the meaning and aspects of creativity. Explain the Investment and Confluence theory of creativity. 2. Explain Sternberg’s Triarchic theory of intelligence. 3. Discuss critically the Innateness theory of language acquisition. SECTION – B Answer the following questions in 400 words each. 5 x 5 = 25 marks 4. Describe the various types of intelligence tests. 5. Explain algorithms and heuristics as strategies of problem solving. 6. Describe the goals and research methods in cognitive psychology. 7. Describe the functions of language. 8. Explain the various speech disorders. SECTION – C Answer the following questions in 50 words each. 10 x 3 = 30 marks 9. Simultaneous and Successive processing 10. Concept of IQ 11. Bodily-kinesthetic intelligence 12. Single-system and dual-system hypotheses in multilingualism 13. Aphasia 14. Functional fixedness 15. Cultural blocks to problem solving 16. Problem space hypothesis 17. Characteristics of difficult problems 18. Luchin’s water jar problem

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IGNOU MPC 006 Solved Assignment 2022-23

IGNOU MPC 006 Solved Assignment 2022-23 : MPC 006 Solved Assignment 2022 , MPC 006 Solved Assignment 2022-23, MPC 006 Assignment 2022-23 , MPC 006 Assignment, IGNOU MPC 006 Solved Assignment 2022-23 IGNOU Assignments 2022-23- Gandhi National Open University had recently uploaded the assignments of the present session for MEG Programme for the year 2022-23. Students are recommended to download their Assignments from this webpage itself.

• 1.1 1. Explain the meaning of descriptive statistics and describe organisation of data.
• 2.1.0.1 Applications of Normal Probability Curve:
• 3.1 3. The scores obtained by four groups of employees on occupational stress are given below. Compute ANOVA for the same.
• 3.2 4. Discuss the assumptions of parametric and nonparametric statistics.
• 3.3 5. Using Spearman’s rank order correlation for the following data:
• 3.4 6. Describe various levels of measurement with suitable examples.
• 4.1 7. Explain Kruskall- Wallis ANOVA test and compare it with ANOVA.
• 4.2 8. Compute Chi-square for the following data:
• 5.1 SECTION – C
• 5.2 9. Type I and type II errors.
• 5.3 10. Skewness and kurtosis.
• 5.4 11. Point and interval estimations.
• 5.5 12. Null hypothesis
• 6.1 13. Scatter diagram
• 6.2 14. Outliers
• 6.3 15. Biserial correlation
• 6.4 16. Variance
• 7.1 17. Interactional effect
• 7.2.1 IGNOU Instructions for the MPC 006 STATISTICS IN PSYCHOLOGY
• 7.3.1 Related

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Submission Date :

• 31st March 2023 (if enrolled in the July 2022 Session)
• 30th Sept, 2023 (if enrolled in the January 2023 session).

SECTION – A

1. Explain the meaning of descriptive statistics and describe organisation of data.

Ans. Descriptive statistics are brief informational coefficients that summarize a given data set, which can be either a representation of the entire population or a sample of a population. Descriptive statistics are broken down into measures of central tendency and measures of variability (spread).

Measures of central tendency include the mean, median, and mode, while measures of variability include standard deviation, variance, minimum and maximum variables, kurtosis, and skewness.

Descriptive statistics summarizes or describes the characteristics of a data set.

Descriptive statistics consists of three basic categories of measures: measures of central tendency, measures of variability (or spread), and frequency distribution.

Measures of central tendency describe the center of the data set (mean, median, mode).

Measures of variability describe the dispersion of the data set (variance, standard deviation).

Measures of frequency distribution describe the occurrence of data within the data set (count).

Descriptive statistics, in short, help describe and understand the features of a specific data set by giving short summaries about the sample and measures of the data. The most recognized types of descriptive statistics are measures of center: the mean, median, and mode, which are used at almost all levels of math and statistics. The mean, or the average, is calculated by adding all the figures within the data set and then dividing by the number of figures within the set.

For example, the sum of the following data set is 20: (2, 3, 4, 5, 6). The mean is 4 (20/5). The mode of a data set is the value appearing most often, and the median is the figure situated in the middle of the data set. It is the figure separating the higher figures from the lower figures within a data set. However, there are less common types of descriptive statistics that are still very important.

People use descriptive statistics to repurpose hard-to-understand quantitative insights across a large data set into bite-sized descriptions. A student’s grade point average (GPA), for example, provides a good understanding of descriptive statistics. The idea of a GPA is that it takes data points from a wide range of exams, classes, and grades, and averages them together to provide a general understanding of a student’s overall academic performance. A student’s personal GPA reflects their mean academic performance.

Measures of central tendency focus on the average or middle values of data sets, whereas measures of variability focus on the dispersion of data. These two measures use graphs, tables and general discussions to help people understand the meaning of the analyzed data.

Measures of central tendency describe the center position of a distribution for a data set. A person analyzes the frequency of each data point in the distribution and describes it using the mean, median, or mode, which measures the most common patterns of the analyzed data set.

Measures of Variability

Measures of variability (or the measures of spread) aid in analyzing how dispersed the distribution is for a set of data. For example, while the measures of central tendency may give a person the average of a data set, it does not describe how the data is distributed within the set.

So while the average of the data maybe 65 out of 100, there can still be data points at both 1 and 100. Measures of variability help communicate this by describing the shape and spread of the data set. Range, quartiles, absolute deviation, and variance are all examples of measures of variability.

Consider the following data set: 5, 19, 24, 62, 91, 100. The range of that data set is 95, which is calculated by subtracting the lowest number (5) in the data set from the highest (100).

In descriptive statistics, univariate data analyzes only one variable. It is used to identify characteristics of a single trait and is not used to analyze any relationships or causations.

For example, imagine a room full of high school students. Say you wanted to gather the average age of the individuals in the room. This univariate data is only dependent on one factor: each person’s age. By gathering this one piece of information from each person and dividing by the total number of people, you can determine the average age.

Bivariate data, on the other hand, attempts to link two variables by searching for correlation. Two types of data are collected, and the relationship between the two pieces of information is analyzed together. Because multiple variables are analyzed, this approach may also be referred to as multivariate.

Let’s say each high school student in the example above takes a college assessment test, and we want to see whether older students are testing better than younger students. In addition to gathering the age of the students, we need to gather each student’s test score. Then, using data analytics, we mathematically or graphically depict whether there is a relationship between student age and test scores.

Descriptive statistics have a different function than inferential statistics, data sets that are used to make decisions or apply characteristics from one data set to another.

Imagine another example where a company sells hot sauce. The company gathers data such as the count of sales, average quantity purchased per transaction, and average sale per day of the week. All of this information is descriptive, as it tells a story of what actually happened in the past. In this case, it is not being used beyond being informational.

Let’s say the same company wants to roll out a new hot sauce. It gathers the same sales data above, but it crafts the information to make predictions about what the sales of the new hot sauce will be. The act of using descriptive statistics and applying characteristics to a different data set makes the data set inferential statistics. We are no longer simply summarizing data; we are using it predict what will happen regarding an entirely different body of data (the new hot sauce product).

Applications of Normal Probability Curve:

Some of the most important applications of normal probability curve are as follows:

The principles of Normal Probability Curve are applied in the behavioural sciences in many different areas.

• NPC is used to determine the percentage of cases in a normal distribution within given limits:

The Normal Probability Curve helps us to determine:

• What percent of cases fall between two scores of a distribution.
• What percent of scores lie above a particular score of a distribution.

iii. What percent of scores lie below a particular score of a distribution.

3. The scores obtained by four groups of employees on occupational stress are given below. Compute ANOVA for the same.

SECTION – B

4. Discuss the assumptions of parametric and nonparametric statistics.

5. using spearman’s rank order correlation for the following data:, 6. describe various levels of measurement with suitable examples..

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7. Explain Kruskall- Wallis ANOVA test and compare it with ANOVA.

8. compute chi-square for the following data:, section – c, 9. type i and type ii errors., 10. skewness and kurtosis., 11. point and interval estimations., 12. null hypothesis, 13. scatter diagram, 14. outliers, 15. biserial correlation, 16. variance, 17. interactional effect, 18. wilcoxon matched pair signed rank test..

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IGNOU Instructions for the MPC 006 STATISTICS IN PSYCHOLOGY

IGNOU MPC 006 Solved Assignment 2022-23  Before attempting the assignment, please read the following instructions carefully.

• Read the detailed instructions about the assignment given in the Handbook and Programme Guide.
• Write your  enrolment number, name, full address and date on the top right corner of the first page of your response sheet (s).
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• Use only foolscap size paper for your response and tag all the pages carefully
• Write the relevant question number with each answer.
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• Planning: Read the questions carefully. IGNOU MPC 006 Solved Assignment 2022-23 Download PDF Go through the units on which they are based. Make some points regarding each question and then rearrange these in a logical order. And please write the answers in your own words. Do not reproduce passages from the units.
• Organisation: Be a little more selective and analytic before drawing up a rough outline of your answer. In an essay-type question, give adequate attention to your introduction and conclusion. IGNOU MPC 006 Solved Assignment 2022-23 Download PDF The introduction must offer your brief interpretation of the question and how you propose to develop it. The conclusion must summarise your response to the question. In the course of your answer, you may like to make references to other texts or critics as this will add some depth to your analysis.
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IGNOU MPC-006 Study Material & Textbook Download

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MPC-006: Statistics in Psychology

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Title : Active carbons as nanoporous materials for solving of environmental problems

However, up to now, the main carriers of catalytic additives have been mineral sorbents: silica gels, alumogels. This is obviously due to the fact that they consist of pure homogeneous components SiO2 and Al2O3, respectively. It is generally known that impurities, especially the ash elements, are catalytic poisons that reduce the effectiveness of the catalyst. Therefore, carbon sorbents with 5-15% by weight of ash elements in their composition are not used in the above mentioned technologies. However, in such an important field as a gas-mask technique, carbon sorbents (active carbons) are carriers of catalytic additives, providing effective protection of a person against any types of potent poisonous substances (PPS). In ESPE “JSC "Neorganika" there has been developed the technology of unique ashless spherical carbon carrier-catalysts by the method of liquid forming of furfural copolymers with subsequent gas-vapor activation, brand PAC. Active carbons PAC have 100% qualitative characteristics of the three main properties of carbon sorbents: strength - 100%, the proportion of sorbing pores in the pore space – 100%, purity - 100% (ash content is close to zero). A particularly outstanding feature of active PAC carbons is their uniquely high mechanical compressive strength of 740 ± 40 MPa, which is 3-7 times larger than that of  such materials as granite, quartzite, electric coal, and is comparable to the value for cast iron - 400-1000 MPa. This allows the PAC to operate under severe conditions in moving and fluidized beds.  Obviously, it is time to actively develop catalysts based on PAC sorbents for oil refining, petrochemicals, gas processing and various technologies of organic synthesis.

Victor M. Mukhin was born in 1946 in the town of Orsk, Russia. In 1970 he graduated the Technological Institute in Leningrad. Victor M. Mukhin was directed to work to the scientific-industrial organization "Neorganika" (Elektrostal, Moscow region) where he is working during 47 years, at present as the head of the laboratory of carbon sorbents.     Victor M. Mukhin defended a Ph. D. thesis and a doctoral thesis at the Mendeleev University of Chemical Technology of Russia (in 1979 and 1997 accordingly). Professor of Mendeleev University of Chemical Technology of Russia. Scientific interests: production, investigation and application of active carbons, technological and ecological carbon-adsorptive processes, environmental protection, production of ecologically clean food.   

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