# Quantitative Designs Discussion-

For this DQ elaborate within 280 words. Use in-text citations accordingly. Use
scholarly reference(s) along with the one(s) attached as well. Use and cite references using APA 7th Style Guide accordingly. Doctoral level class.

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DQ 1) Review Table 6: Quantitative Core Designs and Descriptions in the quantitative dissertation template. Contrast the characteristics of each of the core designs. How each design can be used to address a research question? Support your reasoning.

 Design Description General Requirements Non-Experimental Class Goal is to examine relationships between variables or comparisons among groups, with no treatment or intervention involved. Causal conclusions cannot be reached using non-experimental designs. Correlational or Associative Examines relationship(s) between pairs of variables using data from a single group of participants with the intent of assessing the direction and strength of a relationship. Can use primary (i.e., collected by the learner) and/or secondary data (i.e., not collected by the learner). · Examines relationships between variables in a naturally occurring setting. Variables should not or cannot be manipulated. · There is a theoretical and/or research-based justification for expecting a correlation or association. · Requires valid approaches to data collection such as validated surveys or databases. · Can use categorical (ordinal or nominal) or continuous (interval or ratio) variables. · Data analysis involves some type of correlation or association test. Correlational-predictive Examines relationship(s) between two or more variables using data from a single group of participants, with the intent of predicting a criterion variable from one or more predictor variables. Can use primary (i.e., collected by the learner) and/or secondary data (i.e., not collected by the learner). · Examines relationships between variables in a naturally occurring setting. Variables should not or cannot be manipulated. · There is a theoretical and/or research-based justification for expecting a predictive relationship. · Requires valid approaches to data collection such as validated surveys or databases. · Can use categorical (ordinal or nominal) or continuous (interval or ratio) variables. · Data analysis will require some type of regression. Comparative Examines differences between two or more groups defined by one or more categorical variables and/or between two or more measurements of a single group. Uses primary data (i.e., collected by the learner) and there is no manipulation of variables. · Examines relationships between variables in a naturally occurring setting. Variables should not or cannot be manipulated. · There is a theoretical and/or research-based justification for expecting differences. · Requires valid approaches to data collection such as validated surveys. · Define (choose for comparison) mutually exclusive groups that are as homogeneous as possible. Ex Post Facto Examines differences between two or more groups defined by one or more categorical variables and/or between two or more measurements of a single group. Uses secondary data (i.e., not collected by the learner). · Examines relationships between variables in a naturally occurring setting. Variables should not or cannot be manipulated. · There is a theoretical and/or research-based justification for expecting differences. · Requires data justifiably assumed to be valid and reliable. Experimental Class Goal is to examine the effect(iveness) of some treatment / intervention. True Experimental Examines the effect/outcome of some form of treatment(s) using random assignment of participants to treatment and control groups. The researcher controls both treatment and measurement. Uses primary data. · Two or more equivalent groups to receive one or more clearly defined and controlled treatment(s) and a control group. · Random assignment of participants to each of the groups. · Standardization of all aspects of research procedures employed to ensure conditions are the same for all participants (e.g., control of potential confounding variables). · Categorical independent variable(s) and interval or ratio level dependent variable(s). · Strongest in terms of both internal and external validity. · Strong support for cause-and-effect conclusions. Pre-Experimental Examines the effect/outcome of some form of treatment(s) using either one or two pre-existing group of participants. May use a one-shot comparison group (not a control group measured pre and post). Uses primary data (i.e., data collected by the learner). · Only two such designs generally will be considered: · One Group Pretest-Posttest Design · O X O · A single group is measured both before and after some treatment. No comparison to a non-treatment group is made. · Static Group Comparison Design · O X O O · A group is measured after some treatment, and this result is compared to a measurement from a second group that did not receive the treatment. · Typically, no random assignment – participants are in pre-existing groups or groups that are naturally formed (group inclusion is beyond the control of the researcher). · Conducted with similar rigor and control as experimental studies with clearly defined treatments. · Requires categorical independent variable(s) and ordinal, interval or ratio level dependent variable(s). · Potential for many confounding variables. · Extremely “weak” in terms of both internal and external validity. · Very little support for cause-and-effect conclusions. · Often used for exploratory research. · Should be considered only if other experimental designs are not an option. Quasi-Experimental Examines the effect/outcome of some form of treatment(s) using either one or two pre-existing group of participants. May use a control group measured pre and post. Uses primary data (i.e., data collected by the learner. · The researcher selects the groups to compare and when to make the measurements. · Some example designs: · Non-Equivalent Control Groups Design · O X O · O O · Simple Interrupted Time Series Design · O O O O X O O O O · Typically, no random assignment – participants are in pre-existing groups or groups that are naturally formed (group inclusion is beyond the control of the researcher). · Conducted with similar rigor and control as experimental studies with clearly defined treatments. · Requires categorical independent variable(s) and ordinal, interval or ratio level dependent variable(s). · Potential for many confounding variables. · Better than pre-experiment in terms of both internal and external validity. · Somewhat stronger support for cause-and-effect conclusions. · Should be considered only if a true experiment is not an option.

Quantitative Sample Size Requirements

· The input for the required a priori estimation of the minimum sample needed for the planned analysis (to be performed in G*Power or and equivalent software/service) has to include: (1) the default medium effect size for the planned analysis (unless the learner can support a larger effect size from literature with similar studies using the same instruments); (2) the standard level of statistical significance (alpha = .05) or a corrected value (Bonferroni correction is recommended); and (3) minimum statistical power .80. All other input items depend on the selected analysis and the variable structure examined in the analysis. For analyses/tests not featured in G*Power (or equivalent services), the sample size estimate will be justified based on prior published research.

http://www.gpower.hhu.de/en.html
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· When the recruited sample is smaller than the estimated sample, the learner is expected to include in the G*Power appendix a post hoc computation of the achieved statistical power or of the test sensitivity (i.e., the effect size that could be captured, considering the recruited sample size). This is typically calculated by determing the actual effect size from the study and inputting this value along with the other required values into G*Power for post-hoc analysis.

·
When calculating the expected return rate for questionnaires and surveys, assume the rate will be 5-10% when no incentives are provided and 10-20% when incentives are provided.

· Learners should add at least 15% to the base sample size projection to allow for loss of cases (participants) due to missing data, assumption violations (e.g., outliers), etc..

· For repeated-measures studies, learners should add 20% (on top of the 15%) in anticipation of participant attrition between the repeated measurements.

· Learners who plan to use parametric tests should add 15% to the “final” projected sample size, in case they have to change to a non-parametric analysis.

· Learners need to ensure their target population is large enough to obtain their final sample size.

Reference List

Babbie, E. (2013
). The practice of social research (13thed.). Belmont, CA: Wadsworth Cengage Learning.

Campbell, D.T. & Stanley, J.C. (1963).
Experimental and quasi-experimental designs for research. Chicago, IL: Rand-McNally.

Charmaz, K. (2011).
Constructing grounded theory. Thousand Oaks, CA: Sage Publishing.

Creswell, J. (2012).
Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4thed.). Upper Saddle River, NJ: Pearson Education.

Frost, N. (2011).
Qualitative research methods in psychology: From core to combined approaches. New York, NY: Open University Press, McGraw Hill Education.

Gravetter. F.J. & Forzano, L.B. (2009).
Research methods for the behavioral sciences (4thed.). Belmont, CA: Wadsworth Cengage Learning.

Percy, W. H., Kostere, K., & Kostere, S. (2015). Generic qualitative research in psychology.

Ranjit, K. (2014).
Research methodology: A step by step guide for beginners (3rded.). Thousand Oaks, CA: Sage Publications Inc.

Yin, R.K. (2011).
Qualitative research from start to finish. New York, NY: Guilford Press.

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QUANTITATIVE Core Designs, aligns with v9 Template August 10, 2020