Non experimental%20 quantitative%20research%20designs-1

Non-Experimental Quantitative Research Designs

Chapter 8

Germaine HarleyBart Miller


• Research Design• How information is obtained• The plan and structure of research to provide a credible answer to a research question

• Purpose of Non-Experimental Research• Describes existing phenomena without changing some condition to affect subjects’ responses.• A report of the way things are or the way things were. • Investigates the current status of something.


•Four types of designs- Descriptive- Relationships

ComparativeCorrelational

- Causal-comparative- Ex Post Facto- Survey


•Descriptive Studies• Describes a phenomenon – in the form of stats, percentages, averages, frequencies, etc. • Graphs and Visuals• Book Example – Classroom Climate/Student’s Attitudes & Learning

• Describe what is meant by “classroom climate” – Once established – variables can be related.

• Consumer Tips• Conclusions about relationships should be made with caution

• Subjects and instrumentation should be well described

• Relationships in Non-Experimental Designs– Relationship/Association– Systematic variation between two variables

• Types of Relationship Studies• Comparative• Correlational


WHY ARE RELATIONSHIPS IMPORTANT?


Q #2: Why are relationships important in nonexperimental designs?

Mary: Because relationships describe differences or similarities between variables, they are important to help the researcher identify the variables to study. Also, studying relationships can help researchers to identify causes of, or to predict behaviors or outcomes.

Kristen: There are many reasons relationships are important in non-experimental designs. 1. they allow us to make a preliminary identification of possible causes for the outcomes in the research. 2. they help us indentify variables to be studied. 3. they allow us to predict the value of one variable based on other variables. 4. the research is dominated by the term and, therefore, it is important to know what is meant by it.

Pan: Relationships allow us to identify causes of educational phenomena and can identify variables for research. And they can help us predict the importance of one variable over another. Relationships are very important in research. Research is to examine all sorts of relationships between different things.

• Comparative Studies– Compares two or more groups on a variable.– Example: If math scores for males are significantly higher than those

for females, a relationship exists between gender and math achievement

• Consumer Tips– Subjects instrumentation, and procedures should be well described

– Identify the criteria for establishing different groups

– Do not infer causation from comparative research designs

– Graphic presentations should not distort the results


• Correlational Studies– Two or more variables are related with the use of one or more correlations

coefficients.

– Coefficient expresses the nature of the relationship between the variables.

– Reliability of instrument is important – without it, it is difficult to obtain a significant correlation coefficient.

– Lack of variability in scores (e.g., everyone scoring very, very low; everyone scoring very, very high; etc.) makes it difficult to identify relationships. Restriction in range - when the variability of scores is small, the correlation will be low.


• Prediction Studies- Show how one variable can predict another.

• Predictor Variable – H.S. GPA precedes college GPA• Criterion Variable – The predicted dependent variable

– Multiple Regression Analysis• Provides a single index of the predictive power of all the predictor variables

together in studies with several predictor variables. Combines several predictor variables

– Coefficient of Multiple Correlation• The correlation of all the independent variables (predictor variables) to the

dependent variable (criterion variable)

• Symbol = R


–Consumer Tips– Causation should not be inferred from correlation

• A relationship between two variables (e.g., achievement and attitude) does not mean one causes the other (i.e., positive attitudes do not cause high levels of achievement.

– The reported correlation should not be higher or lower than the actual relationship• Restriction in range• Attenuation – lowering of correlation because of unreliable measures.

– Practical significance should not be confused with statistical significance• (Coefficient of Determination) – the square of the correlation coefficient

– The size of the correlation should be sufficient for the use of the results.– Prediction studies should report accuracy of prediction for new subjects.– Procedures for collecting data should be clearly indicated.


• Causal-Comparative – Ex-Post Facto Studies

• Consumer Tips• Primary purpose of the research should be to investigate causal relationships when an

experiment is not possible• The presumed causal condition should have already occurred in an ex post facto study • Potential extraneous variables should be recognized and considered• Differences between groups being compared should be controlled• Causal conclusions should be made with caution


• Using Surveys in Non-Experimental Research– Define purpose and objectives– Identify resources needed and target population– Choose an appropriate survey method– Word questions carefully– Develop Directions– Develop a letter of transmittal– Pilot test

• Cross-Sectional Survey• Longitudinal Survey• Internet-Based Surveys• Advantages and Disadvantages



Q #1: Describe the difference between cross-sectional and longitudinal surveys. Explain when it would be appropriate/best to use each.

Mary: Longitudinal surveys occur with the same participants over a long period of time. Like studying a 1st grade cohort and then following up with the same group every year for 10 years. This type of survey attempts to look for trends in a group of people.

Cross-sectional survey collects information from samples at one time. This would be like taking a poll of regarding the President's job performance. This is a convenient survey and gives a snap shot of current status of things.

Kristen: A cross-sectional survey is where information is collected from one or more samples or populations at one time. One type studies a specific phenomenon occurring at one time. Another type compares different age categories to study any differences or relationships. Gagging politics would be a good use for a cross-sectional survey or comparing students across different grade levels.

A longitudinal survey is where the same group of subjects is studied over a certain amount of time. Data are collected at different times, often over several years. Longitudinal studies are good when the researcher is interested in how a group responds over that period of time and how they change in their response.


Q #1: Describe the difference between cross-sectional and longitudinal surveys. Explain when it would be appropriate/best to use each.

Pan: Cross-sectional surveys are when you collect information from sample populations at one time. Longitudinal surveys are when similar subjects are interviewed over long periods of time.

It is best to use longitudinal surveys when you are researching trends over a long period of time.Cross sectional surveys are convenient and provide immediate information.

JEOPARDY!

Education

Non experimental%20 quantitative%20research%20designs-1