Research Methods and Ethics Quiz

Experimental Design I: Single-Factor Designs

• Single-factor designs have one independent variable.
• Four varieties of single-factor designs: independent groups, matched groups, nonequivalent groups, and repeated measures.
• Independent groups designs use different groups for each level of the independent variable.
• Matched groups designs try to form equivalent groups by matching characteristics.
• Nonequivalent groups involve groups that are not equivalent. This can be a limitation.
• Repeated measures designs involve the same participants at each level of the independent variable.
• Bar graphs and line graphs are used to present data. Bar graphs are used to show the differences between groups, and line graphs show within-subject data over time.
• Types of control groups used: placebo, wait-list, and yoked control groups.
• The one-way ANOVA is used if you are examining data from an independent-group or multilevel study instead of multiple t-tests. This reduces the possibilities of errors in analysis.
• Post-hoc analyses are commonly used with one-factor ANOVAs. This post-hoc testing is used to ensure all possible comparisons are made between the levels of the independent variable once an overall significant effect has been found.

Ebbinghaus Memory Research

• Ebbinghaus aimed to understand memory processes.
• His methodology focused on self-testing memory.
• Results showed a fast initial rate of forgetting, followed by a slower decline.

Single-Factor-Two Levels

• Between-subjects, single factor designs examine independent groups.
• Manipulated independent variables are deliberately varied to see how they affect the dependent variables.
• Random assignment ensures equivalent groups.
• Examples provided include note-taking methods and social skills training.
• Subject variables are intrinsic characteristics that cannot be manipulated. These may cause nonequivalence between groups.

Ex Post Facto Designs

• Subject variables are used as independent variables.
• Researchers consider how an existing condition impacts a dependent variable without manipulating it.
• Attempts include carefully selecting participants to minimize nonequivalence between groups.
• Examples include studies of traumatic brain injury effects.

Within-subjects, Single-Factor Designs

• Also referred to as repeated-measures designs.
• Participants are included in all levels of the independent variable.
• Often use counterbalancing techniques.
• Examples are the Stroop effect and chocolate preference.

Single-Factor-More Than Two Levels

• Between-subjects, multilevel designs can show non-linear effects.
• Examples include the relationship between arousal and performance, and the impact of contextual information on memory.
• A significant advantage of the multilevel design is to ensure discovering non-linear effects.

Analyzing Data from Single-Factor Designs

• Different graphs are used to represent data, depending on if comparing groups (bar) or within-subject changes over time (line).
• Table 7.2 displays a comparison of recall data.
• Ebbinghaus forgetting curve shows retention over time.

Special-Purpose Control Group Designs

• Placebo control groups are used to isolate the effects of a treatment.
• These groups receive an inactive substance or a control treatment.
• Waiting-list control groups ensure equivalent groups.
• Research Example 17 shows an application of these methods in a study of subliminal weight loss tapes.
• A significant aspect of special-purpose methodologies is ensuring a double-blinding procedure.

Yoked Control Groups

• Each subject in the control group is matched with those in the experimental.
• Examples of use in stress study with EMDR therapy.
• The aim is to account for external variables that could affect the result.

Factorial Designs: Chapter 8

• These involve two or more independent variables.
• Use a standardized notation system (e.g., 2x2, 3x5).
• Data is placed in matrices, and row and column means are calculated.
• Main effects describe the effect of each independent variable.
• Interaction effects show when effect of one independent variable depends on the value of another.
• This chapter includes famous studies (e.g., Jenkins and Dallenbach) as examples.
• It considers different types of factorial designs such as P x E designs. Mixed factorial designs include at least one between-subjects factor and at least one within-subjects factor. A critical aspect of factorial design is counterbalancing.
• The number of participants needed for the study is dependent on whether the factors are between-subjects or within-subjects

Non-Experimental Designs: Chapter 9

• Surveys seek information on attitudes, beliefs, and behaviors.
• Survey design factors: sampling issues, principles, wording, data interpretation, and collection methods.
• Four methods for collecting survey data are examined.
• Correlation analysis examines relationships between variables without inferring cause-and-effect.
• Correlation coefficients (positive, negative, no correlation) are used for the analysis.
• Scatterplots illustrate bivariate relationships graphically.
• Regression analysis predicts variables' scores.
• This chapter discusses interpreting correlational data, including problems like directionality and third variables and solutions such as, cross-lagged panel correlation, and partial correlation.

Test your knowledge on research methodologies and ethical considerations in the field of research. This quiz covers essential concepts such as qualitative research disadvantages and the significance of peer review in publications. Enhance your understanding of research fundamentals and their implications.