Old exam 1 Flashcards
Professors Wedlin and Sallis to some degree represent the poles of the Philosophy of Science spectrum of constructivism (Wedlin) and positivism (Sallis). Why are they both skeptical of the word “facts” and they both emphasize the importance of the word “evidence”?
Research, no matter which perspective, can always have flaws. In statistics, this could be represented by the trade-off between type I and type II error. Conclusions should be based on findings and evidence. Social scientists claiming facts should be treated with skepticism
Which answer best describes a case-study design?
It emphasizes a full contextual analysis of a few events or conditions and their interrelations.
What is a cross-sectional research design and how does it relate to causality?
Data is collected at a specific point in time from a cross-section of respondents, so concluding causal inference is weak.
What is the research design?
It is the plan for how a research project will be conducted.
True experiments with randomly assigned subjects and control groups are unusual in social sciences, including economics and business studies. More often, you will see quasi- experimental designs like the Mexican music and sales of Mexican food example. The subjects (the people shopping) were not randomly assigned to shop in stores with/without Mexican music playing. In 2019 and again in 2021, the Nobel Prize winners for economics employed what is now widely known as “natural experiments”. Professor Wedlin talked about this. What is a natural experiment?
An event occurs to a specific group of people outside the control of the researchers, but in such a way as to resemble random assignment. Data is collected from before and after the event, and causality is established.
Of these statistical methods, which one is associated with a causal research design?
Regression
What is a exploratory factor analysis?
Exploratory Factor Analysis (EFA) is a method used to discover hidden patterns in data. It helps group many variables into smaller sets called factors based on how they are related. These factors represent underlying concepts that can’t be directly measured but influence the observed data. For example, in a survey about habits, EFA might reveal that questions about sleep, exercise, and diet all relate to a single factor like “health.” It’s useful for simplifying complex data and finding meaningful connections.
What statistical analysis would you use to test the average difference between the groups?
Independent samples t-test
What is an independent samples t-test?
An independent samples t-test is a statistical test used to compare the means of two separate groups to see if they are significantly different. The groups must be independent, meaning the people or items in one group are not related to those in the other. For example, you might use this test to compare the average test scores of students from two different classes. It assumes that the data is approximately normal and that the two groups have similar variances. This test helps answer questions like, “Is there a real difference, or is it just due to chance?”
What statistical analysis would you use in this experimental research design to test the effect on the dependent variable?
Regression analysis
What is a repeated samples t-test?
A repeated samples t-test compares the means of two related measurements to see if there’s a significant difference. It’s used when the same subjects are tested twice, like before and after an intervention. For example, you might measure students’ scores before and after tutoring to check if it made a difference. This test looks at the change in each individual and checks if the overall average change is meaningful or just due to chance. It’s great for studying changes over time or under different conditions for the same group.
What is a correlation analysis?
Correlation analysis measures the relationship between two variables to see if they move together. It tells you how strong the connection is and whether it’s positive (both increase together) or negative (one increases while the other decreases). For example, it can show if study hours and test scores are related. The result is a number between -1 and 1, called the correlation coefficient. A value near 1 means a strong positive relationship, near -1 means a strong negative relationship, and 0 means no relationship at all. It’s a simple way to explore connections in data!
What is a t-test?
A t-test is a statistical test that compares the means of groups to see if they are significantly different from each other. It’s useful when you want to check if a difference is real or just happened by chance. For example, you might use a t-test to see if students in two classes scored differently on the same exam. It works by comparing the averages and considering the variation in the data. There are different types, like independent t-tests for separate groups and repeated t-tests for related groups. It’s a simple way to test differences!
What is a reliability analysis?
Reliability analysis checks how consistent and dependable a set of measurements or a test is. It tells you if the tool or method you’re using gives stable results over time or across different conditions. For example, if a survey asks similar questions about happiness, reliability analysis ensures people give consistent answers. A common measure is Cronbach’s Alpha, which shows how well the items in a test relate to each other. High reliability means the test is trustworthy and not influenced by random errors. It’s all about making sure your data is solid and repeatable!
