This session is dedicated to bridging the gap between the formal course requirements and your personal goals as a researcher. We will begin by completing a detailed Student Needs & Background Questionnaire. Your responses will directly influence how we approach the 14 weeks of instruction, allowing us to emphasize the data collection techniques or analytical methods you find most challenging or intriguing.

This introductory lecture will explore why psychology is fundamentally a science, dispelling the myth that it solely relies on common sense or intuition. We will examine why human intuition is often flawed, using examples like the unexpected positive relationship between text messaging abbreviations and children's verbal reasoning skills to highlight the necessity of empirical research. The core of the session will introduce the scientific method, specifically tracing its evolution from the early empirical method of gathering data without preconceptions to the modern hypothetico-deductive model. Students will learn the critical distinction between theories, which are broad explanations, and hypotheses, which are precise, testable statements derived from those theories. A major theme of the lecture is that scientific research never "proves" a theory to be absolutely true; rather, evidence can only support or challenge it. We will discuss the concept of falsifiability, emphasizing that for a theory to be considered scientific, it must be stated in a way that allows it to be proven false. The lecture will also underscore the importance of replication and attempting to disconfirm theories, as testing the limits of an effect is crucial for advancing psychological knowledge. Furthermore, we will break down the practical steps of planning an investigation, focusing on four key interdependent decision areas: identifying variables, selecting appropriate samples, determining the research design, and planning the data analysis. Students will be introduced to the concept of operationalizing variables, ensuring that abstract concepts like aggression are translated into clear, measurable terms. Finally, the lecture will briefly contrast traditional quantitative methods involving numerical data with qualitative approaches that focus on meaningful verbal information, acknowledging the diverse ways psychologists study human behavior.

This lecture provides a foundational overview of how social scientists, particularly psychologists, measure phenomena and select participants for their research. We will begin by exploring the concept of variables, defining them as observable or hypothetical phenomena whose changes can be tracked and quantified. Because many psychological concepts, like anxiety or intelligence, are not directly observable, we will discuss how researchers treat them as hypothetical constructs. To study these constructs objectively, you will learn how to create precise operational definitions that detail the exact steps taken to measure a phenomenon. The critical principles of measurement reliability—meaning consistency—and validity—ensuring an instrument measures what it claims to—will also be introduced as pillars of credible research. The second half of the lecture shifts to the crucial topic of sampling, examining how researchers select representative individuals from a larger target population. We will compare equal probability selection methods, such as simple random and stratified sampling, against non-random techniques like quota, opportunity, and snowball sampling. Furthermore, the lecture will address the complexities of sample size, explaining why a larger sample is not always better and how it relates to statistical power. We will also explore the modern advantages and logistical challenges of sourcing research participants through online platforms. Finally, we will unpack the fundamental theoretical division between quantitative methodologies rooted in positivism and the qualitative approach that emphasizes the rich, subjective meanings of human experience.

The lecture introduces experimental designs in psychology, emphasizing how a true experiment isolates cause and effect by eliminating alternative explanations for observed relationships. Instructors will explain that the core mechanism involves manipulating an independent variable while tightly controlling all other salient variables and recording the subsequent effect on a dependent variable. Additionally, the lecture will clarify how uncontrolled confounding and extraneous variables can obscure these intended effects and threaten the validity of the conclusions. Moving into specific methodologies, the session will contrast independent samples designs—where different groups experience different conditions, risking participant variables—with repeated measures designs. To mitigate these risks in independent designs, the importance of random allocation to conditions, pre-testing, and the use of control or placebo groups to establish baseline measures will be highlighted. The discussion will then address the repeated measures design, where each participant experiences all levels of the independent variable, which minimizes participant variables but introduces order effects like practice or fatigue. The instructor will detail solutions to these order effects, such as counterbalancing, where half the participants complete conditions in a reverse sequence to balance the outcomes. Other approaches, including matched pairs, single participant, and small N designs, will be presented alongside complex factorial designs that manipulate multiple independent variables simultaneously. Towards the end, the lecture will shift to modern online experiments, weighing benefits like testing large, diverse samples against disadvantages such as the lack of control over the testing environment. By the end of the presentation, attendees will understand the inherent strengths and weaknesses of these experimental designs and be equipped to identify causal directions while questioning alternative explanations.

This lecture will explore the fundamental differences between true experiments, quasi-experiments, and non-experimental designs in psychological research. Instructors will explain that quasi-experiments lack essential features of true experiments, such as random allocation to conditions or full experimenter control over the independent variable. The session will heavily contrast laboratory experiments with field studies, highlighting that laboratories provide tight control over extraneous variables but are often criticized for their artificiality and narrow measures. Conversely, field studies allow researchers to observe behavior in naturalistic everyday environments, though they inherently suffer from a loss of control over these extraneous variables. Students will learn about natural experiments, which occur when researchers exploit convenient, naturally occurring events that they have not personally organized. Time-series designs will also be covered, demonstrating how researchers track public or archival data over multiple measurements to detect relatively sudden changes after a specific intervention. The lecture will then transition into non-experimental research, where variables that already exist among people are investigated without any researcher manipulation. A major focus will be group difference studies, which compare existing categories like gender or personality traits, making it difficult to establish clear cause-and-effect relationships. Correlational and observational methods will be discussed to show how researchers identify relationships between variables without implying a causal direction. Ultimately, the presentation will emphasize that while field and non-experimental studies are crucial for applied psychology, they are highly vulnerable to various threats to validity compared to tightly controlled true experiments.

This session will introduce the core principles of psychological data gathering through direct questioning and self-report methods, such as face-to-face, telephone, or internet interviews, contrasting them with experimental and observational techniques. A major focus will be the dimension of structure in interview designs, comparing formal, standardized structured interviews with informal, conversational unstructured approaches that often yield richer, more genuine responses. We will explore this structural choice through the lens of the quantitative-qualitative debate, contrasting the positivist view of extracting objective facts with the qualitative perspective of interviews as social interactions where unique realities are constructed by the participant. Additionally, the session will delve into the critical interpersonal dynamics between the interviewer and interviewee, examining how an interviewer's presentation style, demand characteristics, and researcher expectancies can significantly bias the data collected. Finally, we will review empirical evidence highlighting how demographic variables—specifically the interviewer's gender, age, race, and ethnicity—can directly influence participant nervousness, performance, and the level of disclosure on both sensitive and general topics.

This session explores how psychologists gather quantitative data using questionnaires and psychometric scales to precisely measure characteristics like attitudes and personality. We will examine the predominant use of closed questions and discuss techniques to mitigate social desirability bias, including disguised aims, the bogus pipeline, and projective tests. A major focus is practical scale construction, where you will learn to build Likert, Semantic Differential, and Visual Analogue scales. We will emphasize the importance of using clear, unambiguous items and mixing positive and negative statements to prevent response set bias. Furthermore, we will critically evaluate these instruments by exploring external and internal reliability—using statistical tools like split-half correlation and Cronbach’s alpha—alongside face, content, and construct validity via factor analysis. Finally, the session covers the vital role of standardisation and test norms in preventing cultural and class biases when interpreting individual scores.

This session guides you through selecting statistical tests based on your research aim, measurement level, and study design, alongside planning and systematically writing a psychology report. We emphasize immersing yourself in literature to formulate testable, operationalized hypotheses. You will learn to establish sound methodological designs, pilot materials to ensure equivalence, utilize established psychological scales, and manage ethical data collection, including informed consent and participant anonymity. For the write-up, we outline standard academic conventions and the strict necessity of avoiding plagiarism. You will learn to craft a funnel-shaped introduction moving from broad theory to specific predictions, write a highly replicable method section detailing your procedure, and correctly format results using descriptive and inferential statistics without including raw data. Finally, we cover writing discussion sections that link findings back to original hypotheses, constructively address methodological weaknesses without listing knee-jerk faults, and propose meaningful directions for future research.