InstructionsThe reply must summarize the student’s findings and indicate areas of agreement, disagreement, and improvement.
Below you will find the original discussion post that needs to be replied to
D1.1. Compare the terms active independent variable and attribute independent variable. What are the similarities and differences?
According to the course material, variables are essential elements within research problems. Essentially, variables are the characteristics of the situation or participants in a study that incorporates different values. In quantitative research, variables are operationally and commonly divided into independent variables (active or attribute), dependent variables, and extraneous variables. Thus, active independent variables are imperative but not a sufficient condition to construct cause and effect conclusions. Variables such as a workshop, new curriculum, or other interventions are provided to the participants within a specified timeframe during a study. In addition, these independent variables are easy to manipulate. For instance, randomized experimental and quasi-experimental studies will obtain an active independent variable. Then, attribute or measured independent variables are not easy to influence. However, they remain a significant focal point in a study. Generally, the values of an independent variable are preexisting attributes of the participants or their ongoing environment. Examples include the level of one’s parental education, socioeconomic status, age, ethnic group, IQ, and self-esteem. Studies that solely implement attribute independent variables are called nonexperimental studies. Both independent variables are pivotal as they measure how much a model-predicted value is modified (Morgan et al., 2020). Furthermore, Kaur (2013) states that an active variable in one study could be an attribute variable in another.
D1.2. What kind of independent variable (active or attribute) is necessary to infer cause? Can one always infer cause from this type of independent variable? If so, why? If not, when can one infer cause and when might causal inferences be more questionable?
Fundamentally, independent variables account for the cause of dependent variables. When it comes to cause-and-effect relationships, the concept is not always easy to infer. To ensure that the two variables are related, the following conditions are pivotal: 1. The cause has to precede the effect in time, 2. Empirical relationships transpire between the cause and effect and an observable pattern of covariation between the variables, and 3. Relationships between the two shouldn’t be able to be explained by other factors. In addition, with limitations in inferring cause-and-effect relationships, it is essential to be cautious yet appropriate to utilize the terms cause and effect when examining relationships between variables. Thus, the following are guidelines that identify independent and dependent variables: 1. Dependent variables are the properties within the research, 2. Independent variables generally transpire earlier than dependent variables, and 3. Independent variables influence, directly or indirectly, dependent variables (Frankfort-Nachmias & Leon-Guerrero, 2000). Thus, active variables provide input that allows one to infer that independent variables cause modifications to dependent variables. Essentially, it is not always feasible to relate cause to active independent variables. Overall, causal inferences are questionable when different manipulations are presented to pre-existing groups, especially since pretests of dependent variables should not be conducted before a manipulation (Morgan et al., 2020).
D1.3. What is the difference between the independent variable and the dependent variable?
Dependent variables are assumed to measure and assess the effect of independent variables. This notion is the presumed outcome or criterion. Dependent variables are typically test scores, ratings on questionnaires, readings from instruments, or the measurements of physical performances. Dependent variables, like independent variables, must obtain at least two values and are statistically utilized in cases numerous times (Morgan et al., 2020). In experimental research, independent variables are modified by experimenters that measure the direct effect of the changes to dependent variables. An example includes allocating participants to either drug or placebo conditions (independent variable) to measure changes in the intensity of their anxiety (dependent variable). However, dependent variables are tested and measured in an experiment and are dependent on independent variables. An example of a dependent variable is depression symptoms, which depends on the independent variable, type of therapy (Mcleod, 2019).
D1.4. Compare and contrast associational, difference, and descriptive types of research questions.
Primarily, research questions are similar to hypotheses. However, they do not entail specific predictions and are in a question format of three broad types: difference, associational, and descriptive. Morgan et al. (2020) assert that difference research questions compare two or more different groups, each of which is composed of individuals with one of the values of the independent variable. These questions also seek to display that the groups are not the same as the dependent variable. Next, associational research questions combine two or more variables and generally address how two or more variables covary. An example includes an individual obtaining a higher value in one variable as well as in another variable. An associational question determines how one or more variables enables one to predict another variable. Regarding descriptive research questions, they cannot be answered with inferential statistics. These questions merely describe and summarize data to studied samples without seeking to generalize to a larger population of individuals (Morgan et al., 2020).
D1.5. Write a research question and a corresponding hypothesis regarding variables of interest to you but not in the HSB dataset. Is it an associational, difference, or descriptive question?
(Associational) What is the significant correlation, if any, between the standardized tests, PSAT and ACT, and an approaching high school graduate’s IQ?
Several researchers assert that the PSAT and ACT are prime measures of intelligence to assess a student’s academic potential and performance with fellow college contenders and applicants. However, other researchers do not acknowledge the association between one’s IQ and test scores. This notion arises from the belief that intelligence would decrease consumers’ reliance that scores could improve with adequate coaching offered for admission to ivy league colleges and universities (Brown, 2019).
D1.6. Using one or more of the following HSB variables, religion, mosaic pattern test, and visualization score:
D1.6(a). Write an associational question.
What effects, if any, does one’s mosaic pattern test or any other standardized tests possess on his or her religion?
D1.6(b). Write a difference question.
What religious concerns threaten one’s mosaic pattern test as opposed to their visualization score?
D1.6(c). Write a descriptive question.
What are the benefits or expected outcomes of conducting a mosaic pattern test?
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