Quantitative Research Concepts and Strategies Quantitative research strategies
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Quantitative Research Concepts and Strategies
Quantitative research strategies are driven by two concerns.
Quantitative research strategies are driven by two concerns. Quantitative research is interested in the nature of relationships among variables.
Quantitative research strategies are driven by two concerns. Quantitative research is interested in the nature of relationships among variables. Quantitative researchers are interested in whether their discoveries are generalizable.
Quantitative research is interested in the nature of relationships among variables. Variable A Variable B
The variables might be unrelated. Variable A Variable B
The variables might be correlated. Variable A Variable B
One variable might affect another. Variable A Variable B
When one variable affects another, Variable A Variable B
When one variable affects another, they are given specific labels.
When one variable affects another, they are given specific labels. Independent Variable Dependent Variable
The term “quantitative” refers to this research approach because we wish to quantify these two concepts: -The size of the relationships among variables. - The probability that the results are generalizable.
-The size of the relationships among variables. - The probability that the results are generalizable.
-The size of the relationships among variables. - The probability that the results are generalizable.
-The size of the relationships among variables. This is quantified using mathematics: - The probability that the results are generalizable.
-The size of the relationships among variables. This is quantified using mathematics: The difference in average scores between males and females on the SAT. The correlation between scores on an IQ test and grade point average. - The probability that the results are generalizable.
-The size of the relationships among variables. This is quantified using mathematics: The difference in average scores between males and females on the SAT. The correlation between scores on an IQ test and grade point average. - The probability that the results are generalizable. This is quantified using inferential statistics:
-The size of the relationships among variables. This is quantified using mathematics: The difference in average scores between males and females on the SAT. The correlation between scores on an IQ test and grade point average. - The probability that the results are generalizable. This is quantified using inferential statistics: “There is a statistically significant difference at the. 05 level between males and females on the SAT. ”
Inferential statistics procedures actually provide both quantities of interest for us- the size of the relationship and the probability that the relationship exists in the larger population the researcher’s sample is meant to represent.
The particular statistical procedure that is used depends on two things: § The number of independent and dependent variables. § The level of measurement used for those variables.
There are four levels of measurement:
There are four levels of measurement: Nominal Numerical values are used only as names for different categories.
There are four levels of measurement: Nominal Numerical values are used only as names for different categories. Ordinal The attributes can be rank-ordered. However, distances between attributes do not have any meaning.
There are four levels of measurement: Nominal Numerical values are used only as names for different categories. Ordinal The attributes can be rank-ordered. However, distances between attributes do not have any meaning. Interval The distances between scores have meaning and are treated as equal. For example, when we measure temperature, the distance from 30 -40 is equal to the distance from 70 -80. The interval between values is interpretable.
There are four levels of measurement: Nominal Numerical values are used only as names for different categories. Ordinal The attributes can be rank-ordered. However, distances between attributes do not have any meaning. Interval The distances between scores have meaning and are treated as equal. For example, when we measure temperature, the distance from 30 -40 is equal to the distance from 70 -80. The interval between values is interpretable. Ratio There is an absolute zero that is meaningful. In social science research most "count" variables are ratio, for example, the number of children eligible for special education services.
There are four levels of measurement: Nominal Numerical values are used only as names for different categories. Ordinal The attributes can be rank-ordered. However, distances between attributes do not have any meaning. Interval The distances between scores have meaning and are treated as equal. For example, when we measure temperature, the distance from 30 -40 is equal to the distance from 70 -80. The interval between values is interpretable. Ratio There is an absolute zero that is meaningful. In social science research most "count" variables are ratio, for example, the number of children eligible for special education services.
Group Designs Whether you can trust the results of quantitative research depends on the design that was used. The use of groups and group comparisons is a key design element that supports valid conclusions about the nature of the relationships among variables and the generalizability of results.
Group Designs Whether you can trust the results of quantitative research depends on the design that was used. The use of groups and group comparisons is a key design element that supports valid conclusions about the nature of the relationships among variables and the generalizability of results.
Validity of Quantitative Research Conclusions Issues of Cause and Effect Statistical Conclusion Validity Internal Validity Issues of Generalizability Construct Validity External Validity
Statistical Conclusion Validity Internal Validity Construct Validity External Validity
Statistical Conclusion Validity Internal Validity Construct Validity External Validity Is there a relationship between A & B?
Statistical Conclusion Validity Is there a relationship between A & B? Internal Validity Is there a cause and effect relationship between A & B? Construct Validity External Validity
Statistical Conclusion Validity Is there a relationship between A & B? Internal Validity Is there a cause and effect relationship between A & B? Construct Validity Is the cause and effect relationship between A & B? External Validity
Statistical Conclusion Validity Is there a relationship between A & B? Internal Validity Is there a cause and effect relationship between A & B? Construct Validity Is the cause and effect relationship between A & B? External Validity Is the relationship between A and B generalizable?
The particular statistical procedure that is used depends on two things: § The number of independent and dependent variables. § The level of measurement used for those variables.
The particular statistical procedure that is used depends on three things: § The number of independent and dependent variables. § The level of measurement used for those variables.
The particular statistical procedure that is used depends on three things: § The number of independent and dependent variables. § The number of groups. § The level of measurement used for those variables.
For example: The particular statistical procedure that is used depends on three things: § The number of independent and dependent variables. § The number of groups. § The level of measurement used for those variables.
For example: If you have 1 independent variable and 1 dependent variable and they are both measured at the interval level, you look for a relationship by using a correlation coefficient.
For example: If you have 1 independent variable and 1 dependent variable and they are both measured at the nominal level, you look for a relationship by using a chi-square.
For example: If you have 1 independent variable and 1 dependent variable and the independent variable is at the nominal level and the dependent variable is at the interval level, you look for a relationship by using an independent t test.
For example: If you have 1 independent variable and 1 dependent variable and the independent variable is at the nominal level and the dependent variable is at the interval level, you look for a relationship by using an independent t test. But if the independent variable has more than 2 groups, you use analysis of variance.
And so on…
What I left out…
The variables must be measured with validity and reliability. There are some sampling methods which are better than others in getting a representative sample. Randomly assigning participants to groups solves a lot of problems. There assumptions about how your scores are distributed which must be true before you can trust your statistical results.
Quantitative Research Concepts and Strategies
- Key concepts of qualitative research
- Promotional concepts and strategies
- Contrast trade promotions and consumer sales promotions
- Operationalized research question examples
- Ualitative
- Words images objects qualitative or quantitative
- Similarities between qualitative and quantitative research
- Similarities between qualitative and quantitative research
- Sampling methods in qualitative and quantitative research
- What are the limitations of qualitative research
- Examples of mixed methods research
- Difference between qualitative and quantitative research
- Key concepts in research
- Basic concepts of research
- Holistic analysis in qualitative research
- Research design for qualitative research
- Research design sample
- Narrative research design
- Hypothesis template
- Descriptive study design
- Descriptive kind of quantitative research
- Stage of research process
- Data types in quantitative research
- Chapter 3 research example qualitative
- Example of independent variable in quantitative research
- Anova in research
- Statistical treatment
- Quantitative research about cycling
- Theoretical framework hypothesis
- Types of methodologies
- Types of quantitative research questions
- Conceptual phase of quantitative research
- Conclusive design
- Scope and delimitation of food safety
- Non experimental quantitative research
- Disadvantage of quasi experimental design
- How to make hypothesis in quantitative research
- Types of quantitative research designs
- Weaknesses of qualitative research
- Codebook quantitative research
- Regression analysis in quantitative research
- Quantitative research in advertising
- Wethnography
- Lesson 1 introduction to quantitative research
- Formula for quantitative research
- Quantitative sociological research
- Examples of researchable and non researchable topics