Quantitative Research Designs Quiz Questions

The mean refers to the sum of the values of a variable for a set of observations divided by the number of observations in the set. It is a measure of central tendency that represents the average value of the data. The mean is calculated by adding up all the values and dividing by the total number of observations.

The probability of making a Type II error in any statistical test is termed as Beta. This error occurs when the null hypothesis is not rejected, even though it is false. In other words, it is the probability of failing to detect a true effect or relationship in the data. Beta is directly related to the power of the test, which is the probability of correctly rejecting the null hypothesis when it is false. Therefore, a smaller value of Beta indicates a more powerful test.

This answer is true because a quasi-experimental research design involves manipulation of the independent variable, but does not have a control group or randomization. In a quasi-experimental design, the researcher cannot randomly assign participants to groups, so they have limited control over the variables. However, they can still manipulate the independent variable to observe its effects on the dependent variable. Without a control group or randomization, it is difficult to establish a cause-and-effect relationship between the variables.

Beneficence is the correct answer because it refers to the ethical principle of promoting the well-being and welfare of study participants. It emphasizes the importance of maximizing benefits and minimizing potential harm to participants. This principle ensures that researchers prioritize the best interests of participants and take steps to protect their rights and well-being throughout the study. By upholding the principle of beneficence, researchers strive to create a balance between the potential benefits of the study and any potential risks or harm to participants.

Meta-analysis is a method of integrating the findings of prior research studies using statistical procedures. It involves systematically collecting and analyzing data from multiple studies to draw conclusions about the overall effect or relationship between variables. By combining the results of individual studies, meta-analysis provides a more comprehensive and reliable estimate of the true effect size or relationship. It allows researchers to identify patterns, trends, and inconsistencies across studies, and can help to resolve conflicting or inconclusive findings. Meta-analysis is widely used in various fields of research to synthesize and summarize existing evidence.

The Hawthorne effect refers to the phenomenon where individuals modify their behavior simply because they are aware that they are being observed or included in a study. This change in behavior can often obscure the true variable of interest being studied, as the participants may alter their actions to align with what they believe the researchers want to see. This effect highlights the importance of considering the impact of participant awareness and reactivity when conducting research.

Trend studies involve the investigation of samples from a general population over a period of time to observe and analyze changes in a specific phenomenon. This type of research aims to identify patterns, trends, and developments over time, providing valuable insights into the long-term effects of various factors. By studying a representative sample of the population, researchers can make informed predictions and recommendations based on the observed trends.

Assumptions are basic principles that are accepted as being true without proof or verification. They are beliefs or ideas that are taken for granted and form the basis of reasoning or decision-making. Assumptions are made based on logic or reason and are not necessarily proven to be true. They are often used as starting points in various fields such as science, philosophy, and mathematics, and serve as foundational principles for further exploration or analysis.

A flow diagram is a visual representation of a process or system, showing the flow of information, materials, or actions. It is typically used to illustrate the sequence of steps or the movement of objects in a process. Flow diagrams are not typically drawn from quantitative data, as they focus more on the flow and movement rather than numerical values. On the other hand, frequency curves, scatter diagrams, and histograms are all graphical representations that can be created from quantitative data to analyze patterns, relationships, and distributions. Therefore, the correct answer is flow diagram.

A Type I error refers to a false positive result, where the null hypothesis is rejected when it is actually true. In this case, the pharmacology trial concluded that the new antipsychotic reduced negative symptoms, but in reality, it did not. Therefore, the trial incorrectly rejected the null hypothesis and made a Type I error by concluding that the antipsychotic was effective when it was not.

Statistical regression, also known as regression to the mean, explains the phenomenon where individuals who obtain extreme scores on a pretest are likely to obtain less extreme scores on a posttest. This occurs because extreme scores are often influenced by random factors that are unlikely to persist over time. As a result, individuals who initially scored extremely high or low are likely to move closer to the average on subsequent measurements. This phenomenon is not related to internal validity, statistical power, or statistical validity.

Carry-over effects refer to the influence of a previous condition or treatment on the subsequent condition or treatment in a study. In a repeated measures design, the same participants are exposed to multiple conditions or treatments, allowing for the examination of carry-over effects. This design is particularly useful when studying within-subject changes over time or when controlling for individual differences. Therefore, the correct answer is repeated measures design.

A bar diagram is the best graph for demonstrating the relationship between ages and weight because it allows for easy comparison between different age groups and their corresponding weights. The bars represent the different age groups, and the height of each bar represents the weight for that age group. This visual representation makes it easy to identify any patterns or trends in the data, such as whether weight tends to increase or decrease with age. Additionally, a bar diagram can accommodate a large amount of data and is suitable for both categorical and numerical data.

The power of a statistical test refers to the probability of correctly rejecting the null hypothesis when it is false. In other words, it is the likelihood of detecting a true effect or relationship between variables. It is influenced by factors such as sample size, the level of significance chosen for the test, and the magnitude of the effect being studied. Therefore, the power of a statistical test is the most appropriate explanation for the given answer.

Internal validity refers to the extent to which a study can confidently attribute the observed effects to the independent variable, rather than to confounding factors or chance. It ensures that the relationship between the independent and dependent variables is not due to any other extraneous variables. In other words, internal validity ensures that the observed effects are genuine and not a result of random or spurious relationships.

Bracketing is not related to dealing with extraneous variables in quantitative research. Bracketing is a method used in qualitative research to acknowledge and minimize the researcher's biases and preconceived notions. It involves self-reflection and awareness of the researcher's own beliefs and perspectives. However, when it comes to dealing with extraneous variables in quantitative research, methods such as randomization, repeated measures, homogeneity, and blocking are commonly used to control and minimize their impact on the study's results.

The Delphi technique is a method of data collection that involves multiple rounds of questionnaires being sent to a panel of experts. The experts are asked to provide their opinions or judgments on a specific topic of interest. This iterative process allows for the experts to review and revise their responses based on the feedback from other panel members. The Delphi technique is often used when there is a need to gather insights from a diverse group of experts and reach a consensus on a particular issue.

Maturation refers to the changes that occur naturally over time in the participants of a study, which can potentially influence the results. These changes could be due to physical, psychological, or social factors that are unrelated to the independent variable being studied. Maturation is considered a threat to internal validity because it can confound the results by causing changes in the dependent variable that are not actually caused by the independent variable. This can make it difficult to determine if the observed effects are truly a result of the manipulation or simply due to natural maturation processes.

Biophysiologic measures have better subjectivity compared to the other data collection methods listed. This is because biophysiologic measures involve collecting data directly from the body, such as heart rate, blood pressure, or brain activity. These measures provide objective and accurate information about the physiological state of the subject, without relying on subjective interpretation or self-reporting biases. In contrast, self-reports, observation, and questionnaires are more susceptible to subjective biases and may be influenced by individual perceptions, memory, or social desirability.

Analysis of Variance (ANOVA) is a multivariate statistical approach, which means it involves the analysis of multiple dependent variables simultaneously. ANOVA is used to compare the means of three or more groups to determine if there are any statistically significant differences between them. Therefore, the correct answer is "Analysis of Variance" as it is a multivariate statistical approach.