Statistical Methods for Psychological Research - II

By Dr. Tirupathi Rao Padi | Pondicherry University (A Central University)

Learners enrolled: 571

The concept of statistical inference is the most popular mechanism in exploring the interpretations on several research hypothesis. Meaningful conclusions on the study of hypotheses can be arrived through these contents. The intended objective is to address the part-2 of statistical inference, i.e. Testing of Statistical hypotheses more suited with research in psychological studies. Hence, this course has the significance scope on usage of different statistical techniques for psychological research methodology.

This course is designed by keeping in mind about the students of Graduate (Hons) of the universities and other HEIs. It has given focus on the parametric as well as non-parametric testing of hypotheses. Further, the syllabus contents are designed to deal with the tests of the varying sample sizes namely small sample tests, exact sample tests and large sample tests. The inference methods on the relational measures like correlation between variables and association between the attributes are included. As the current trends are with the machine assisting statistical computing, the basic required data handling procedures are discussed through SPSS. Different aspects such as handling of data processing begin with from the data collection to the report making are also covered.

Inference procedures that consists of (i) defining research objectives, (ii) formulating the null and alternative hypothesis, (iii) defining the formula for the test statistic, (iv) computing the test statistic for the data under illustration, (v) finding the test critical value for the given level of significance, required degrees of freedom and the type of the test (one tailed/ two tailed), (vi) drawing the conclusions on the objectives of the test, etc. are discussed elaborately. The procedures through chi-square test, t- test, F test and Z- test, etc. for handling the tests of proportions, Means, variances, correlation coefficients, etc. are explained in detail by adopting the conceptual understanding through numerical examples.

About 50 modules are designed with due weightage on the contents of diversified categories like (i) Familiarity on the terminology, (ii) theory behind test procedures, (iii) understanding the test mechanism through illustrations, (iv) computing through usual statistical methodology by means of using scientific calculators, (v) statistical computation through software such as MS Excel, SPSS, etc. This course material is prepared with the student’s perception in mind by following straight and simple explanations, learner friendly approaches, practicing with numerical examples, etc. Hope the students of non-mathematical orientation will enjoy the course contents by getting in out of the essential concepts.

Course Objectives:

After completion of this course, the students are enable with good concepts of statistics for dealing the statistical inference, testing the statistical hypotheses phase with all the effective lines. This online course is having the objectives of

1. To provide the essential knowledge of statistical science for dealing the research methodology in Psychology.

2. Statistical Inference is the main focus with more emphasis on the testing of statistical hypothesis

3. To impart the knowledge on the handling procedures of Both parametric and non-parametric tests in the contexts of Small and large sample size cases.

4. To provide the theoretical and conceptual understanding of different statistical tests with illustrations

5. To train the students through problem solving and enable them for having proper understanding of statistical methods for psychological research.

Course Outcome:

The course entitled “Statistical Methods for Psychological Research -2” is designed with the view of extending the following outcome.

1. Overview and detailed descriptions on different terminology and glossary of the Testing of hypothesis

2. Detailed understanding on the Null and the Alternative Hypotheses Research Hypothesis

3. Test procedures with on Small and Large sample tests namely t-test, F-test, Chi-square Test and Z-test

4. Detailed understanding of Parametric and Non-Parametric Testing of statistical Hypotheses

5. Numerical examples on tests for Proportions, Means, Variances, Correlation Coefficients, Good Ness of Fit, Independence of Attributes, ANOVA

6. Statistical computing with MS Excel and SPSS

Summary

Course Status :	Ongoing
Course Type :	Core
Duration :	15 weeks
Category :	Humanities and Social Sciences
Credit Points :	5
Level :	Undergraduate
Start Date :	15 Jul 2024
End Date :	31 Oct 2024
Enrollment Ends :	31 Aug 2024
Exam Date :	08 Dec 2024 IST
Shift :	Shift 2

Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week 1

Overview on Inferential Statistics

Hypothesis Testing with Small Sample Sizes (t test)

Hypothesis Testing with Large Sample Sizes

Assumptions in Hypothesis Testing for a Single Mean; the Null and the Alternative Hypotheses

Week 2

Choice of Ha One-Tailed and Two-Tailed Tests; Steps for Hypothesis Testing

Hypothesis Testing about a Single Mean and Calculation

The Statistical Decision Making Regarding Retention and Rejection of Null Hypothesis

Estimating the Standard Error of the Mean when σ is Unknown

Week 3

t Sampling Distribution - Description, Characteristics, Computing with Definitional Formula

Overview of Statistical Inference and Hypothesis Testing

Critical Value, Significance Level and p-value

A Statistically Significant Difference versus a Practically Important Difference

Week 4

Errors in Hypothesis Testing and Power of a Test

Hypothesis Testing about the Difference between Two Independent Mean - An Overview

Null and Alternative Hypotheses; the Random Sampling Distribution of the Difference between Two Sample Means

Week 5

Properties of Sampling Distribution of the Difference Between Two Sample Means

Determining a Formula for t-distribution

Testing the Hypothesis of No Difference between Two Independent Means; Use of One-Tailed Test

Week 6

Assumptions Associated with Inference about the Difference between Two Independent Means

Hypothesis Testing about the Difference Between Two Dependent (Correlated) Means – An Overview

The Null and Alternative Hypotheses for Testing the Difference between Two Dependent (Correlated) Means

Week 7

Determining a Formula for t in Testing the Differences Between Two Dependent (Correlated) Means

Determining t test statistic, Properties and Degrees of Freedom for Tests of No Difference Between Dependent Means

Testing a Hypothesis about Two Dependent Means using the Formula Involving Standard Errors and Correlation Only

Assumptions when Testing a Hypothesis about the Difference between Two Dependent Means

Week 8

Null Hypothesis and Alternative Hypothesis

Hypothesis Testing for Differences among Three or more Groups

Basis, Assumptions within and between - Group Variances of One-Way ANOVA

Week 9

Partition of Sum of Squares and One-Way ANOVA Procedure

Application of One-Way ANOVA

Post-Hoc Comparisons in One-Way ANOVA

Raw Score Formula and Comparison of t and F Test

Week 10

Hypothesis Testing for Categorical Variables and Inference about Frequencies

The Chi-Square as a Measure of Discrepancy between Expected and Observed Frequencies

Logic of the Chi-Square Test; Assumptions of Chi-Square

Week 11

Calculation of the Chi-Square Goodness of Fit Test One-Way Classification

Chi-Square for Two-Way Classification Variables - Contingency Table Analysis

Interpretation of the Outcome of a Chi-Square Test

Week 12

Non-Parametric Approach To Data Analysis

Non-Parametric Test for One Sample and Two Independent Samples

Non-Parametric Test for Two Dependent Samples

Week 13

Non-Parametric Tests for More Than Two Samples

Introduction to SPSS

Structure of SPSS

Week 14

Getting Started with SPSS

Formatting of Excel Data Platform

Exporting Excel To SPSS

Week 15

Formatting of SPSS Platform

Exploring Descriptive Statistics in SPSS

Exploring Different Statistical Graphs and Tables

Books and references

1. Dennis Howitt & Duncan Cramer, Research Methods in Psychology, (6th Edition) (2020)

2. Arthur Aron, Elliot J. Coups, Elaine N. Aron; Statistics for Psychology (2019

3. David C. Howell; Statistical Methods for Psychology, 8th Edition (2012).

4. Rajamanickam M., Statistical Methods in Psychological and Educational Research (2001)

5. K. Kalyanaraman, Statistical Methods for Research: A Step by Step Approach Using IBM SPSS, (2018)

Instructor bio

Dr. Tirupathi Rao Padi

Pondicherry University (A Central University)

Dr. Tirupathi Rao is currently working as the Professor, Department of Statistics, Pondicherry University form 4th January 2012 onwards. It is his 3rd academic institute. He has worked with Sri Venkateswara University, Tirupati from 9.7.2007 to 3.1.2012; at Mrs. A.V.N. College, Visakhapatnam, A.P. (A government Aided College under the UGC scales of pay from 20th November 1988 to 8th June 2007. Her was formerly the Dean, Ramanujan School of Mathematical Sciences, Head of the department of statistics and Pondicherry University. He had around 105 Research papers publication under his credit, guided 12 Ph.D. awardees, published on Patent in the month of October 2023 and registered for the second Patent in March 2024. He is affiliated with 14 Research and scientific forums as life member. He is in around 30 editorial boards for research paper review in different journals with international repute. He Served the Bureau of Indian standards as Principal Member from 2010 to 2015. He has a total of 35 years of teaching experience for the students of UG, PG and doctoral program students in Statistics. He taught the course of the statistics to the diversified students community that deals with Mathematical Statistics, Bio-statistics, Management Statistics, Statistics for Social sciences, Statistics for Engineering, MCA, MBA and other similar.

Course certificate

To obtain Course Certificate: 30 marks will be allocated for Internal Assessment and 70 marks will be allocated for end term proctored examination. Securing 40% in both separately is mandatory to pass the course and get Credit Certificate.

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