Author Archives: Samantha Shang

1. Generalization mode: Researchers are attempting to make External Validity (this study to the world at large) crutial. That is, to generalize the findings to a population, or other situations. Ecological Validity (lab to the real world) is also important. The key is to have a representative sample. 2. Theory-testing mode: Researchers are trying toContinue reading “Class #22 and the last: Wrap-up”

1. Inflated Type I error: For each test, there’s a α=5% probability that the difference between groups is not meaningful (i.e. due to chance). If we have n groups to compare, and we keep running t-tests, there will be 1-(1-α)n chance to make a Type I error! 2. To control for Type I error: MakeContinue reading “Class #21: ANOVA”

1. When the probability that your results are due to chance/sampling variation is small enough (p <.05), we reject the null, then these results are called statistically significant. We also mentioned statistically significance during Class #14 Correlation. 2. T-test: tells us the difference between the means of two groups, and an estimate of what differences areContinue reading “Class #20: Data analysis (t-test)”

1. Null Hypothesis (H0, pronounced as H-nought): The independent variable will not have an effect; there’s no difference between treatment group and control group. Alternative Hypothesis (H1): The independent variable has an effect. When we try to confirm the hypothesis, we are statistically testing the Null Hypothesis. We assume H0 is true (there is noContinue reading “Class #19: Thinking about Probabilities (Testing the Null Hypothesis)”

1. One-way design: manipulating only one independent variable 2. Factorial design: manipulating two or more independent variablesDescribing the size and structure of factorial designs: 2 × 2 factorial design: 2 IVs; 2 levels for each 3 × 3 factorial design: 2 IVs; 3 levels for each 2 × 2 × 4 factorial design: 3 IVs;Continue reading “Class #18: More on Experimental Design”

1. Again, group designs: Randomized Group Design (Between-subjects): each participant is only assigned to ONE condition Repeated Measures Design (Within-subjects): each participants gets to be in each condition 2. Repeated Measures (Within-subjects) Design: Strengths: Doesn’t need random assignment Has Initial Equivalence (because every subject is the same in each condition) More powerful than Between-subjects design,Continue reading “Class #17: Experimental Design”

1. What makes studies experimental?– Researcher manipulates one variable (the Independent Variable) – Researcher controls the group assignment (experimental groups + control group [baseline])– Researcher controls extraneous variables that influnce participants’ behavior (confounds) 2. In experimental studies, we are interested in how one (or more) variable [Independent Variable] changes behavior [Dependent Variable]. NOT just howContinue reading “Class #16: Experimental Research”

1. Regression analysis: drawing a straight line through the scatter plot, mathematically. Regression analysis can be used to predict the relationship between 2 or more variables. 2. Simple Linear Regression: y = B0 + B1 x  e.g.: Predicting your grade of PSY310 from the frequency of checking this blog. >:) 3. Multiple Regression Analysis: predicting the outcomeContinue reading “Class #15: Regression”

1. Interpreting correlations: Because 1) We don’t know the direction; 2) There could be a third variable involved. 2. Statistical Significance* (p<= .05) for correlation coefficient means: the correlation calculated on the sample has a very low probability of being .00 in the population.In other words, when p<.05, it’s unlikely that the correlation was notContinue reading “Class #14: More on Correlation”

1. Correlations answer questions of the relationship between two variables. Scatter plots is one way of determining the form of relationship. 2. Correlation coefficient (r) quantify the linear relationship between two variables.Pearson r: A statistical index of how 2 variables systematically relate to one another in a linear fashion. Range: -1 to +1 Sign (direction):Continue reading “Class #13: Correlation”