Quantitative Research Concepts and Strategies Quantitative research strategies

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Quantitative Research Concepts and Strategies

Quantitative Research Concepts and Strategies

Quantitative research strategies are driven by two concerns.

Quantitative research strategies are driven by two concerns.

Quantitative research strategies are driven by two concerns. Quantitative research is interested in the

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

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

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 unrelated. Variable A Variable B

The variables might be correlated. Variable A Variable B

The variables might be correlated. Variable A Variable B

One variable might affect another. 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, Variable A Variable B

When one variable affects another, they are given specific labels.

When one variable affects another, they are given specific labels.

When one variable affects another, they are given specific labels. Independent Variable Dependent Variable

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

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

-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

-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

-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

-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

-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

-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

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

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:

There are four levels of measurement: Nominal Numerical values are used only as names

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

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

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

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

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

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

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 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

Statistical Conclusion Validity Internal Validity Construct Validity External Validity Is there a relationship between

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

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

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

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

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

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

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: §

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

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

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

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

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…

And so on…

What I left out…

What I left out…

The variables must be measured with validity and reliability. There are some sampling methods

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

Quantitative Research Concepts and Strategies