Lecture 5-7: Data Collection and Analysis PDF

Lecture 5: Data Collection Key Concepts and Definitions: 1. Levels of Measurement: o Nominal Scale: Classifies data into distinct categories without any order.  Example: Gender (Male = 1, Female = 2) or eye color (Blue, Green, Brown). o O...

Lecture 5: Data Collection Key Concepts and Definitions: 1. Levels of Measurement: o Nominal Scale: Classifies data into distinct categories without any order.  Example: Gender (Male = 1, Female = 2) or eye color (Blue, Green, Brown). o Ordinal Scale: Categorizes data with an inherent order, but intervals are not consistent.  Example: Satisfaction levels in a survey, where 1 = “Very Unsatisfied,” 2 = “Unsatisfied,” 3 = “Neutral,” 4 = “Satisfied,” 5 = “Very Satisfied.” o Interval Scale: Data with equal intervals between points but no true zero.  Example: Temperature in Celsius. The difference between 10°C and 20°C is the same as between 20°C and 30°C, but 0°C does not mean "no temperature." o Ratio Scale: Like interval data, but with a true zero, allowing for comparisons like "twice as much."  Example: Height in centimeters or age in years. A 20-year-old is twice as old as a 10-year-old. 2. Data Collection Methods: o Survey: Structured questions to gather quantitative data from many participants.  Example: A questionnaire asking students about their weekly study hours. o Observation: Gathering qualitative data by watching and recording behavior without intervention.  Example: Observing students' interactions in a classroom to understand social dynamics. o Interview: Collecting in-depth qualitative data through direct questioning, useful for detailed insights.  Example: Interviewing students individually to explore their study habits and motivations. o Experiment: A controlled setup where researchers manipulate variables to examine cause-and-effect relationships.  Example: Testing the impact of a study guide on exam performance by providing it to one group (experimental) and not to another (control). Lecture 6: Data Analysis (Qualitative) Key Concepts and Definitions: 1. Qualitative Data Analysis: o Thematic Analysis: Identifies and reports recurring themes in qualitative data.  Example: Analyzing interview transcripts from students discussing study stress, where themes like “time management issues” and “exam anxiety” emerge.  o Coding: Assigning labels to data segments to categorize and make sense of patterns.  Example: Marking mentions of “stress,” “sleep,” and “focus” in responses to identify major factors affecting study. o Discourse Analysis: Examines language to understand social and psychological aspects of communication.  Example: Analyzing how students describe stress in different ways, like “pressure” vs. “overwhelming,” to explore how they internalize academic demands. o Interpretive Phenomenological Analysis (IPA): Focuses on individuals' personal experiences and how they interpret these.  Example: Examining diary entries from students who experience academic burnout to understand their coping mechanisms. Lecture 7: Reporting Findings Key Concepts and Definitions: 1. Errors and Bias: o Sampling Bias: Occurs when the sample is not representative of the broader population.  Example: Conducting a survey only among high-performing students about general study habits can bias results. o Measurement Bias: Arises when data collection tools consistently distort results.  Example: Using an outdated self-esteem questionnaire that does not accurately reflect modern definitions of self-esteem. o Response Bias: When participants respond inaccurately, often influenced by social desirability.  Example: Students may overstate study hours to appear more diligent when surveyed. 2. Objectivity: The researcher’s effort to maintain neutrality and avoid influencing results with personal beliefs. o Example: When analyzing data, a researcher should avoid letting personal opinions on study stress affect their interpretation of findings. 3. Reflexivity: The process of reflecting on how a researcher’s background or bias may affect the research process. o Example: A researcher who struggled with academic stress might need to carefully check for biases when interpreting students’ stress levels. Lecture 8: Ethics in Research and Theory Building Key Concepts and Definitions: 1. Ethical Principles:  o Informed Consent: Ensures participants understand the study’s purpose, risks, and benefits.  Example: Providing students with detailed information on a study about test anxiety before they participate. o Confidentiality: Protecting participants' private information and ensuring it remains secure.  Example: Storing student data from a survey on a secure, password- protected system. o Right to Withdraw: Participants have the freedom to leave the study at any point without any consequence.  Example: If a student feels uncomfortable during a study interview, they can end their participation without needing to explain. 2. Theory Building: o Theory: A framework that organizes and explains phenomena to make predictions.  Example: A theory that suggests a positive relationship between study time and academic performance. o Pattern Recognition: Identifying recurring trends in data that can lead to hypothesis formation.  Example: Noticing that students with more regular study schedules report lower stress levels. o Hypothesis: A testable prediction often derived from theory, stating an expected relationship between variables.  Example: Hypothesizing that students who study regularly will have lower stress than those who cram. Lecture 9: Introduction to Statistics (Quantitative Data) Key Concepts and Definitions: 1. Frequency Distributions: o Ungrouped Frequency Distribution: Shows frequencies of individual values in a dataset.  Example: Counting how many students scored 70, 75, or 80 on an exam. o Grouped Frequency Distribution: Organizes data into intervals to simplify large datasets.  Example: Grouping test scores into ranges (0-50, 51-70, 71-100) to see how many students fall within each range. o Percentage Calculation: Calculating the relative frequency of each category as a percentage of the total sample. Percentage=(fN)×100\text{Percentage} = \left( \frac{f}{N} \right) \times 100Percentage=(Nf)×100 Example: If 20 out of 50 students passed, the percentage is 2050×100=40%\frac{20}{50} \times 100 = 40\%5020×100=40%. Lecture 10: Graphic Representation of Frequency Distributions Key Concepts and Definitions: 1. Graph Types: o Bar Graph: Represents categorical data with bars; each bar’s height represents frequency.  Example: A bar graph showing the number of students in each year of study. o Histogram: Visualizes continuous data with adjacent bars indicating the frequency of data within specific ranges.  Example: A histogram of students' test scores grouped by intervals. o Frequency Polygon: A line graph connecting midpoints of class intervals to show data trends.  Example: A frequency polygon of gym attendance, connecting points at the midpoints of attendance ranges. 2. Midpoint (Class Mark): The central value of each class interval, calculated as: Midpoint=Lower limit+Upper limit2\text{Midpoint} = \frac{\text{Lower limit} + \text{Upper limit}}{2}Midpoint=2Lower limit+Upper limit o Example: If a class interval is 20-30, the midpoint is 20+302=25\frac{20 + 30}{2} = 25220+30=25. Lecture 11: Measures of Central Tendency and Variability Key Concepts and Definitions: 1. Measures of Central Tendency: o Mean: The average of a dataset, calculated by summing all values and dividing by the number of values. Mean=∑ValuesNumber of values\text{Mean} = \frac{\sum \text{Values}}{\text{Number of values}}Mean=Number of values∑Values  Example: Test scores of 10, 20, 30 yield 10+20+303=20\frac{10 + 20 + 30}{3} = 20310+20+30=20. o Median: The middle value in an ordered dataset.  Example: For scores 10, 20, 30, 40, the median is 20+302=25\frac{20+30}{2} = 25220+30=25. o Mode: The most frequent value in a dataset.  Example: In 5, 5, 6, 7, the mode is 5. 2. Measures of Variability: o Range: The difference between the highest and lowest values. Range=Highest value−Lowest value\text{Range} = \text{Highest value} - \text{Lowest value}Range=Highest value−Lowest value  Example: For scores 10 and 50, the range is 50−10=4050 - 10 = 4050−10=40. o Variance: The average squared deviation from the mean. Variance=∑(Value−Mean)2Number of values\text{Variance} = \frac{\sum (\text{Value} - \text{Mean})^2}{\text{Number of values}}Variance=Number of values∑(Value−Mean)2   Example: For scores 10, 20, and 30 with mean 20, variance is (10−20)2+(20−20)2+(30−20)23\frac{(10-20)^2 + (20-20)^2 + (30- 20)^2}{3}3(10−20)2+(20−20)2+(30−20)2. o Standard Deviation: The square root of the variance, showing average distance from the mean. Lecture 12: Correlation and Regression Key Concepts and Definitions: 1. Correlation: o Correlation Coefficient (r): Measures the strength and direction of the linear relationship between two variables, ranging from -1 to +1.  Example: An rrr of 0.8 indicates a strong positive correlation between study hours and test scores. 2. Regression: o Regression Equation: Predicts the value of one variable based on another. Y^=a+bX\hat{Y} = a + bXY^=a+bX  Example: Predicting a student’s satisfaction score based on their gym attendance