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.

I. Introduction to Statistics -- Definition: Statistics is a branch of mathematics focused on organizing, analyzing, and interpreting groups of numbers. -- Purpose: It is a tool for pursuing truth and determining the likelihood that an intuition or hunch is true. -- Benefits: Learning statistics helps students read research articles, conduct their own research, and improve general reasoning and intuition. II. Two Branches of Statistical Methods -- Descriptive Statistics: Used to summarize and describe a group of numbers from a research study. -- Inferential Statistics: Used to draw conclusions and make inferences based on data that go beyond the numbers themselves (e.g., inferring things about a large group based on a small study). III. Basic Concepts: Variables, Values, and Scores -- Definitions: --- Variable: A condition or characteristic that can vary (have different values), such as stress level or height. --- Value: A number (e.g., 4) or category (e.g., female). Score: A particular person's value on a specific variable. -- Types of Variables (Levels of Measurement): --- Numeric (Quantitative): numbers indicating "how much" of something. --- Equal-interval: Differences between numbers represent equal amounts of the thing being measured (e.g., GPA). --- Rank-order (Ordinal): Numbers represent relative ranking, where the difference between ranks may not be equal (e.g., class standing). --- Categorical (Nominal): Values are names or categories rather than numbers (e.g., gender, major). IV. Frequency Tables -- Purpose: A table that shows how frequently each specific value occurs in a dataset to make patterns easier to see. -- Construction: List all possible values from lowest to highest. Mark a tally for each score next to its value. Sum the marks to find the frequency. Calculate the percentage of the total scores for each value. Grouped Frequency Tables: Used when there are too many specific values to list individually. Scores are combined into intervals (e.g., 0–4, 5–9) to simplify the visual picture. V. Histograms -- Definition: A type of bar chart used to graph frequency tables where the height of each bar represents the frequency of a value. -- Visual difference: In histograms (for numeric variables), bars are placed next to each other without spaces. For categorical variables, the graph is called a bar graph, and spaces are left between the bars. VI. Shapes of Frequency Distributions -- Distributions are described by their shape, symmetry, and tail thickness. -- Modality (Peaks): --- Unimodal: One high point. --- Bimodal: Two fairly equal high points. --- Rectangular: All values have roughly the same frequency. -- Symmetry and Skew: --- Symmetrical: Two halves of the graph look the same. --- Skewed: Lopsided with a long "tail" on one side. The direction of the skew (left or right) is determined by the side with the tail. --- Floor/Ceiling Effects: Skews caused by scores piling up at a lower limit (floor effect) or upper limit (ceiling effect). -- Kurtosis (Tail Thickness): --- Normal Curve: A bell-shaped, unimodal, symmetrical curve. --- Heavy-tailed: Thicker tails and more peaked than a normal curve. --- Light-tailed: Thinner tails and flatter than a normal curve. VII. Application in Research -- Reporting: Research articles rarely print full frequency tables or histograms; they usually describe the distribution shapes in the text. -- Software: Researchers typically use statistical software (like SPSS) to generate these tables and graphs rather than doing them by hand