introduction to research methods

Introduction to the Experimental Method: Experiments vs. Non-Experiments and the Role of True Experiments.

Laboratory Experiments: An Introduction to Controlled Scientific Investigation and Their Role in Psychology.

Field Experiments: An Introduction to Controlled Research in Natural Settings and Their Role in Psychology.

Natural Experiments: Exploring Research Where the IV is Not Manipulated and Their Role in Psychology.

"Introduction to Data Handling, Analysis, and Inferential Testing in Psychology"

Understanding data is crucial in psychology to make sense of human behaviour and mental processes. This involves organising, analysing, and interpreting data to draw meaningful conclusions. Data handling includes descriptive statistics like measures of central tendency (mean, median, mode) and dispersion (range, standard deviation).

Inferential testing, on the other hand, helps psychologists determine whether results are due to chance or a real effect. Key topics include hypothesis testing, probability levels, significance, and the selection of appropriate statistical tests (e.g., chi-square, Mann-Whitney, or Spearman's rank). This process ensures psychological research remains scientific and credible.

Quasi-Experiments: Understanding Research Without Random Allocation of Participants.

Experimental Designs in Psychology: Matched Pairs, Independent Groups, Repeated Measures, Counterbalancing, and Random Allocation.

Non-Experiments in Psychology: Research Methods Used When Ethical Concerns or Complex Variables Make Experiments Impossible.

Observational Techniques in Psychology: Exploring Types (Naturalistic, Controlled, Covert, Overt, Participant, Non-Participant) and Design Methods (Behavioural Categories, Event Sampling, Time Sampling).

Questionnaires and Interviews in Psychology: Self-Report Techniques, Structured and Unstructured Formats, and Effective Design with Open and Closed Questions.

Correlations in Psychology: Exploring Co-Variables, Positive, Negative, and Zero Correlations, Correlation Coefficients, and Their Distinction from Experiments.

Case Studies in Psychology: In-Depth Exploration of Individuals or Groups in Non-Experimental Research.

Content, Thematic, and Discourse Analysis: Exploring Qualitative Research Methods in Psychology.

"Data Handling and Analysis: From Descriptive Statistics to Meta-Analysis"

Explore the basics of data handling and analysis in psychology, including descriptive statistics, measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), and the differences between qualitative and quantitative data. Learn how psychologists use meta-analysis to combine and interpret findings across multiple studies. Perfect for students seeking to strengthen their grasp of psychological research methods.

"Descriptive Statistics: A Beginner’s Guide to Understanding and Calculating Key Measures"

Dive into the fundamentals of descriptive statistics in psychology. Learn about measures of central tendency, including the mean, median, and mode, and how to calculate them. Understand measures of dispersion, such as the range and standard deviation, along with how to calculate these values. The guide also covers percentage calculations, helping students analyse and interpret data effectively for psychological research.

THE SIGN TEST

Discover the simplicity of the sign test, a non-parametric statistical method perfect for analysing repeated-measures data. This test helps determine whether there's a significant difference between two conditions by comparing positive and negative changes in scores. Ideal for psychology studies with nominal data, the sign test focuses on differences without requiring complex calculations. Learn how to apply this essential tool to your research for clear and accurate results.

A BEGINNER'S GUIDE TO INFERENTIAL STATISTICS

Inferential statistics allow psychologists to go beyond describing data and make predictions or draw conclusions about a population based on a sample. This beginner’s guide introduces key concepts like hypothesis testing, probability, and p-values. Learn how psychologists determine whether their findings are statistically significant and how inferential methods like t-tests, ANOVAs, and chi-square tests help uncover patterns in psychological research. Perfect for those starting out in psychology, this guide simplifies the process of making sense of complex data.

CONTINUOUS AND CATEGORICAL DATA

Data in psychology can be categorised as continuous or categorical (also called discrete). Continuous data refers to numerical values that can take any value within a range, such as height, weight, or reaction time. It is measured on a scale and often analysed using means or standard deviations.

Categorical data, on the other hand, involves distinct categories or groups. Examples include gender, ethnicity, or types of therapy. This data is non-numerical and is often summarised using frequencies or percentages.

Understanding the distinction is key to choosing the right statistical tests and interpreting psychological research effectively.