Research Methods II PDF

Research Methods II Course Instructor: Ms. Sana Khalid Advantages  Uses a Smaller Sample Size It does not require a large pool of participants. A similar experiment in a between-subject design requires twice as many participants as a within-subject design when two or more groups of participant...

Research Methods II Course Instructor: Ms. Sana Khalid Advantages  Uses a Smaller Sample Size It does not require a large pool of participants. A similar experiment in a between-subject design requires twice as many participants as a within-subject design when two or more groups of participants are tested with different factors.  Reduces Errors Caused by Individual Differences It can help reduce errors associated with individual differences. In a between- subject design where individuals are randomly assigned to the independent variable or treatment, there is still a possibility that there may be fundamental differences between the groups that could impact the experiment's results. Each participant serves as their own baseline. Disadvantages Time-related effects There are many time-related threats to internal validity that only apply to within- subjects design because it’s hard to control the effects of time on the outcomes of the study.  Some examples: History: an unrelated event (e.g., a lockdown) may influence the outcomes. Maturation: the natural physical or psychological changes (e.g., growth or aging) in the participants over time may cause the outcomes. Subject attrition: more participants drop out at every subsequent step of the study, leaving you with a potentially biased sample at the end because only participants with strong motivations stay in the study  Carryover effects  Carryover effects are a broad category of internal validity threats that occur when an earlier treatment alters the outcomes of a later treatment.  Some examples: Practice effects (learning): familiarity with the study based on earlier conditions leads to better performance in later conditions. Order effects: the placement of a condition in a number of conditions changes the outcomes—for example, participants pay less attention in the last condition because of boredom and fatigue. Sequence effects: the interaction between conditions (based on their sequence) affects the outcomes; for instance, participants in an ad rating survey may compare later ads to earlier ones and base their decisions on the sequence of items. Counter Balancing  Counterbalancing is a technique used to deal with order effects when using a repeated measures design. With counterbalancing, the participant sample is divided in half, with one half completing the two conditions in one order and the other half completing the conditions in the reverse order.  For example, let's say your study for depression had two treatments: counseling and meditation. You would split your treatment group into two, giving one group counseling, then meditation. The second group would receive meditation first, then counseling. Factorial Design  By definition, a factorial design involves any study with more than one independent variable (the terms ‘‘independent variable’’ and ‘‘factor’’ mean the same thing).  In principle, factorial designs could involve dozens of independent variables, but in practice these designs usually involve two or three factors, sometimes four. Identifying Factorial Design  First, a factorial is described with a numbering system that simultaneously identifies the number of independent variables and the number of levels of each variable.  Thus a 2 × 3 (read this as ‘‘two by three’’) factorial design has two independent variables: The first has two levels; the second has three.  A 3 × 4 × 5 factorial has three independent variables, with three, four, and five levels, respectively.  The hypothetical memory study would be a 2 × 2 design, with two levels of the ‘‘type of training’’ independent variable (imagery and rote repetition) and two levels of the ‘‘presentation rate’’ independent variable (2 and 4 seconds per item).  Second, the total number of conditions to be tested in a factorial study can be identified by looking at all possible combinations of the different levels of each independent variable. In our hypothetical memory study, this produces a display called a factorial matrix.  In all experimental designs, the term ‘‘levels’’ refers to the number of levels of the independent variable. In factorial designs, the term ‘‘conditions’’ equals the number of cells in a matrix like the one you just examined.  Hence, the 2 × 2 memory study has two independent variables, each with two levels. It has four different conditions, however, one for each of the four cells. The number of conditions in any factorial design can be determined simply by calculating the product of the numbers in the notation system. Thus, a 3 × 3 design has nine conditions; a 2 × 5 has ten conditions, and a 2 × 2 × 2 has eight conditions.  The number of digits tells you how many independent variables (IVs) there are in an experiment, while the value of each number tells you how many levels there are for each independent variable.  So, for example, a 4×3 factorial design would involve two independent variables with four levels for one IV and three levels for the other IV. Example Imagine that a researcher wants to do an experiment looking at whether sleep deprivation hurts reaction times during a driving test. If she were only to perform the experiment using these variables–the sleep deprivation being the independent variable and the performance on the driving test being the dependent variable–it would be an example of a simple experiment. However, let’s imagine that she is also interested in learning if sleep deprivation impacts the driving abilities of men and women differently. She has just added a second independent variable of interest (sex of the driver) into her study, which now makes it a factorial design. Example 2  Suppose a study on the effects of different types of online learning environments and study strategies on academic performance in college students is carried out. The study could use a 2×2 factorial design, with two independent variables (learning environment and study strategy) and two levels of each independent variable.  The learning environments could be synchronous online learning (i.e., live classes with real-time interaction), asynchronous online learning (i.e., pre- recorded lessons with discussion boards), or a combination. The study strategies are self-regulated learning (i.e., self-paced and self-directed study), collaborative learning (i.e., group work and peer feedback), or a combination.  Participants would be assigned groups: synchronous learning with self- regulated study, asynchronous understanding with collaborative study, synchronous and asynchronous learning with self-regulated study, or synchronous and asynchronous learning with collaborative research. The dependent variable would be the participants’ academic performance, measured by their grades in a specific course or course.  By manipulating the levels of the learning environment and study strategy and measuring their combined and individual effects on academic performance, this study could provide valuable insights into the most effective approaches to online learning for college students. Factorial Matrix Types of effects  Main effect  Interaction effect Advantages 1. Ability to investigate multiple factors: These allow researchers to investigate the effects of various independent on a dependent variable in a single experiment, which can save time and resources. 2. Identification of main effects and interactions: These enable researchers to identify the main products of each independent variable and any interaction effects between them, providing a more nuanced understanding of the relationships between variables. 3. Increased statistical power: By manipulating multiple independent variables, factorial designs can increase the statistical power of a study and improve the likelihood of detecting meaningful effects. 4. Flexibility: These adapt to various research questions and uses in multiple fields, including psychology, education, medicine, and engineering Disadvantages 1. Increased complexity: Using multiple independent variables can interpret results more complexly, mainly when interaction effects are present. 2. Increased sample size requirements: A larger sample size is preferable to a more straightforward design with fewer independent variables to power such a design adequately. 3. Potential for confounding: The presence of interaction effects can make it challenging to determine which independent variable is responsible for observed effects, potentially confounding the results. 4. Limited generalizability: The specific conditions of it may not be generalizable to other contexts.