Do not miss the opportunity to take this Postgraduate diploma with us and you will notice how you will become more capable every day to help your students"

The main objectives of the Postgraduate diploma in Data Collection in Educational Research are to promote and strengthen the skills and abilities of teachers, taking into account the most current tools for teaching. This is done in such a way that the teacher is able to inspire his students with the necessary motivation to continue their studies and to feel drawn to scientific research.  

This Postgraduate diploma  provides teachers with an overview of the fundamental knowledge in the field of teaching and the best way to guide and orient students in their day-to-day work.  

This training is distinguished by its order and distribution of theoretical material, guided practical examples in all its modules, and motivational and explanatory videos. This allows for a simple and clarifying study on educational research.  

This way, the main methodologies in the field of educational research will be explained to the student, starting from the main and most reliable techniques for data collection. Additionally, the training continues with Item Response Theory (IRT) and, to conclude, it focuses on multivariate analysis. 

A high-level program that will represent a process of improvement, not only professionally, but also personally. This challenge is one that TECH Global University takes on as a social commitment: to help prepare highly qualified professionals and develop their personal, social and professional skills throughout the course of their studies.  

Not only does it cover the theoretical knowledge offered, but it also shows another way of studying and learning, one which is more organic, simpler and more efficient. TECH works to keep you motivated and to create a passion for learning. And it will push you to think and develop critical thinking.

Expand your knowledge through this Postgraduate diploma in Data Collection in Educational Research It will allow you to improve your CV and the way you deliver your lessons"

This Postgraduate diploma in Data Collection in Educational Research contains the most complete and up-to-date educational program on the market. The most important features include: 

• Case studies presented by experts in educational research
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional development
• Latest development in Data Collection in Educational Research
• Practical exercises where self-assessment can be used to improve learning
• Special emphasis on innovative methodologies in Data Collection in Educational Research
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an internet connection

This Postgraduate diploma is the best investment you can make when selecting a refresher program to update your knowledge in Data Collection in Educational Research" 

Its teaching staff includes professionals belonging to the field of innovation in education, who bring their work experience to this training, as well as renowned specialists from reference societies and prestigious universities. 

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive training programmed to train in real situations. 

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise throughout the program. For this purpose, the teacher will have the help of an innovative system of interactive videos made by recognized experts, with great experience in Data Collection in Educational Research

If you want to train with the best teaching methodology and multimedia, this is your best option"

This Postgraduate diploma is 100% online, which will allow you to combine your professional work with your private life, while increasing your knowledge in this field"

The structure of the contents has been designed by the best professionals in the field of Educational Research, with extensive experience and recognized prestige in the profession, backed by the volume of cases reviewed and studied, and with extensive knowledge of new technologies applied to teaching.

We have the most complete and up-to-date programme on the market. We offer you the best, at the best price" 

Module 1. Data Collection Techniques and Instruments and Measurement

1.1. Measurement in Research

1.1.1. Introduction
1.1.2. What do we Want to Measure?
1.1.3. Subject Measurement Process
1.1.4. Psychometry

1.2. Collection of Information using Quantitative Techniques: Observation and Surveys

1.2.1. Introduction
1.2.2. Observation

1.2.2.1. Theoretical Framework and Categories of Observation

1.2.3. The Survey

1.2.3.1. Material for Conducting a Survey
1.2.3.2. Survey Research Design

1.3. Collection of Information with Quantitative Techniques: the tests

1.3.1. Introduction
1.3.2. Test Concept
1.3.3. Item Generation Process
1.3.4. Testing by Area: Performance; Intelligence and Aptitude; Personality, Attitudes and Interests.

1.4. Collection of Information with Quantitative Techniques: Scaling Methods

1.4.1. Introduction
1.4.2. Concept of Attitude Scales
1.4.3. Thurstone Method

1.4.3.1. Method of Paired Comparisons

1.4.4. Likert Scale
1.4.5. Guttman Scale

1.5. Test Construction Process

1.5.1. Introduction
1.5.2. Item Scaling Process

1.5.2.1. Item Generation Process
1.5.2.2. Information Gathering Process
1.5.2.3. Scaling Process in the Strict Sense

1.5.3. Scale Evaluation Process

1.5.3.1. Item Analysis
1.5.3.2. Scale Dimension
1.5.3.3. Scale Reliability
1.5.3.4. Scale Validity

1.5.4. Subjects' Scores on the Scale

1.6. Analysis of Test Items

1.6.1. Introduction
1.6.2. Classical Test Theory (Spearman, 1904)
1.6.3. Test Reliability
1.6.4. The Concept of Validity
1.6.5. Evidence of Validity

1.7. Reliability of the Instrument

1.7.1. Introduction
1.7.2. Definition of Reliability
1.7.3. Reliability by Test-Retest or Repeatability Method
1.7.4. Reliability by the Alternate or Parallel Shape Method
1.7.5. Reliability Through Internal Consistency Coefficients

1.7.5.1. Kunder-Richardson Coefficient
1.7.5.2. Cronbach's Alpha Coefficient

1.8. Validity of the Instrument

1.8.1. Introduction
1.8.2. Definition of Validity
1.8.3. Validity of the Instruments

1.8.3.1. Immediate Validity
1.8.3.2. Content Validity
1.8.3.3. Construct Validity
1.8.3.4. Contrast Validity

1.8.4. Validity Strategies

1.9. Item Analysis

1.9.1. Introduction
1.9.2. Item Analysis
1.9.3. Difficulty and Validity Indexes
1.9.4. Correction of Random Effects

1.10. Interpretation of Test Scores

1.10.1. Introduction
1.10.2. Interpretation of Scores
1.10.3. Normative Test Scales
1.10.4. Typical Derived Scales
1.10.5. Interpretations Referring to the Criterion

Module 2. Item Response Theory (IRT)

2.1. Item Response Theory (IRT)

2.1.1. Introduction
2.1.2. Measurement Models
2.1.3. Fundamental Concepts of IRT
2.1.4. Basic Postulates of IRT

2.2. Generalizability Theory (GT)

2.2.1. Introduction
2.2.2. Generalizability Theory (GT)
2.2.3. Facets of Generalizability Theory
2.2.4. Interpretation of Results in a Study

2.3. Characteristics of IRT (I)

2.3.1. Introduction
2.3.2. Historical Introduction of TRI
2.3.3. IRT Assumptions
2.3.4. IRT models

2.4. Characteristics of IRT (II)

2.4.1. Introduction
2.4.2. TRI Results

2.4.2.1. Parameters. 2.4.2.2. Item Characteristic Curve
2.4.2.3. True Score
2.4.2.4. Test Characteristic Curve
2.4.2.5. Level of Information

2.4.3. Response Models: the Item Characteristic Curve
2.4.4. Question Selection Methods

2.5. Response Models for Dichotomous Items: the Rasch Contribution

2.5.1. Introduction
2.5.2. The Rasch Model
2.5.3. Characteristics of the Rasch Model
2.5.4. Example (Rasch Model)

2.6. Response Models for Dichotomous Items: the Rasch Contribution

2.6.1. Introduction
2.6.2. Birnbaum's Logistic Model (1968)
2.6.3. Model Parameters

2.6.3.1. 2-parameter Logistic Model
2.6.3.2. 3-parameter Logistic Model
2.6.3.3. 4-parameter Logistic Model

2.7. Response Models for Polytomous Items: Nominal Item Models (Block, 1972)

2.7.1. Introduction
2.7.2. Polytomous Items
2.7.3. Nominal Response Models (Block, 1972)
2.7.4. Political Item Parameters

2.8. Response Models for Polytomous Items: Ordinal Item Models

2.8.1. Introduction
2.8.2. Ordinal Item Models
2.8.3. Ordinal Cumulative Model

2.8.3.1. Samejima's Graded Response Model (GRM) (1969)
2.8.3.2. Modified Graded Response Model (M-GRM) of Muraki (1990)

2.8.4. Continuous Ordinal Models

2.8.4.1. Sequential Model (Tutz, 1990)

2.8.5. Adjacent Ordinal Models

2.8.5.1. Partial Credit Model (Masters, 1982)

2.9. Response Models for Polytomous Items: Samejima's Graded Response Model (1969)

2.9.1. Introduction
2.9.2. Normal Graded Response Model
2.9.3. Graded Response Logistic Model
2.9.4. Example (Graduated Response Model)

2.10. Differential Item Functioning (DIF)

2.10.1. Introduction
2.10.2. Item Differential Concept (DIF)
2.10.3. Types of DIF
2.10.4. DIF screening methods
2.10.5. Purification methods

Module 3. Multivariate Analysis

3.1. Multivariate Analysis

3.1.1. Introduction
3.1.2. What is Multivariate Analysis?
3.1.3. The Objectives of Multivariate Analysis
3.1.4. Classification of Multivariate Techniques

3.2. Multiple Linear Regression

3.2.1. Introduction
3.2.2. Concept of Multiple Linear Regression
3.2.3. Conditions for Multiple Linear Regression
3.2.4. Predictors to Generate the Best Model

3.3. Binary Logistic Regression

3.3.1. Introduction
3.3.2. Binary Logistic Regression Concept
3.3.3. Model adjustment

3.3.3.1. Model fitting in R

3.3.4. Stages of the R
3.3.5. Example (Binary Logistic Regression)

3.4. Nominal and Ordinal Logistic Regression

3.4.1. Introduction
3.4.2. General Review of Nominal Logistic Regression
3.4.3. Example (Nominal Logistic Regression)
3.4.4. General Review of Ordinal Logistic Regression
3.4.5. Example (Ordinal Logistic Regression)

3.5. Poisson Regression

3.5.1. Introduction
3.5.2. Poisson Concept
3.5.3. Distribution Functions
3.5.4. Poisson Regression with Counts

3.6. Log-Linear Models

3.6.1. Introduction
3.6.2. Log-Linear Models for Contingency Tables
3.6.3. Log-Linear Models for Contingency Tables
3.6.4. Example (Log-Linear Models for Contingency Tables)

3.7. Discriminant Analysis

3.7.1. Introduction
3.7.2. Concept of Discriminant Analysis
3.7.3. Classification with Two Groups

3.7.3.1. Fisher Discriminant Function

3.7.4. Example (Discriminant Analysis)

3.8. Cluster Analysis

3.8.1. Introduction
3.8.2. Concept of K-means Clusters
3.8.3. Hierarchical Cluster Analysis Concept
3.8.4. Example (Hierarchical Cluster Analysis)

3.9. Multidimensional scaling

3.9.1. Introduction
3.9.2. Multidimensional Scaling: Basic Concepts
3.9.3. The Similarity Matrix
3.9.4. Classification of Scaling Techniques

3.10. Factor Analysis

3.10.1. Introduction
3.10.2. When is Factor Analysis Used?
3.10.3. Factor Analysis Methodology
3.10.4. Applications of Factor Analysis

This will provide key training to advance your career"