Podcast
Questions and Answers
Which of the following is a type of factorial design?
Which of the following is a type of factorial design?
- Completely randomized design (correct)
- Before-and-after without control
- Randomized block design (correct)
- All of the above
Working hypotheses are not needed when conducting exploratory research.
Working hypotheses are not needed when conducting exploratory research.
True (A)
What is the main function of a research design?
What is the main function of a research design?
To provide for the collection of relevant evidence efficiently.
Research purposes can be grouped into four categories: Exploration, Description, Diagnosis, and __________.
Research purposes can be grouped into four categories: Exploration, Description, Diagnosis, and __________.
Match the types of designs with their appropriate characteristics:
Match the types of designs with their appropriate characteristics:
What is a characteristic of exploratory research designs?
What is a characteristic of exploratory research designs?
What type of design is best for minimizing bias and maximizing reliability when describing a situation?
What type of design is best for minimizing bias and maximizing reliability when describing a situation?
Experimental designs are exclusively formal and do not include informal designs.
Experimental designs are exclusively formal and do not include informal designs.
What is the primary purpose of analyzing interaction effects in factorial designs?
What is the primary purpose of analyzing interaction effects in factorial designs?
In a factorial design, interaction effects can exist between more than two factors.
In a factorial design, interaction effects can exist between more than two factors.
What is a two-way ANOVA used for?
What is a two-way ANOVA used for?
Factorial designs are characterized by the manipulations of ____ or more independent variables.
Factorial designs are characterized by the manipulations of ____ or more independent variables.
Match the following types of factorial designs with their descriptions:
Match the following types of factorial designs with their descriptions:
Which of the following statistical tests is commonly used to evaluate the significance of interaction effects in a factorial design?
Which of the following statistical tests is commonly used to evaluate the significance of interaction effects in a factorial design?
In a factorial design, higher-order interactions are always of primary interest.
In a factorial design, higher-order interactions are always of primary interest.
What is a key assumption made in ANOVA regarding the groups being compared?
What is a key assumption made in ANOVA regarding the groups being compared?
Which sampling method ensures that every item in the population has an equal chance of being included in the sample?
Which sampling method ensures that every item in the population has an equal chance of being included in the sample?
Judgment sampling allows for the inclusion of a random selection of participants from the population.
Judgment sampling allows for the inclusion of a random selection of participants from the population.
What is the primary feature of convenience sampling?
What is the primary feature of convenience sampling?
In ______ sampling, researchers use their judgment to select items believed to be representative of the population.
In ______ sampling, researchers use their judgment to select items believed to be representative of the population.
Match the following sampling methods with their descriptions:
Match the following sampling methods with their descriptions:
Which of the following is NOT a type of probability sampling?
Which of the following is NOT a type of probability sampling?
Stratified sampling divides the population into homogeneous groups before sampling.
Stratified sampling divides the population into homogeneous groups before sampling.
Name one advantage of using simple random sampling.
Name one advantage of using simple random sampling.
Flashcards
Probability Sampling
Probability Sampling
Sampling methods where each item has a known chance of being included in the sample, based on techniques like simple random sampling, systematic sampling, stratified sampling, and cluster sampling.
Working Hypotheses
Working Hypotheses
Tentative explanations or assumptions about the research subject, derived from prior knowledge, data analysis, expert opinions, and related studies. They are crucial for guiding the research process.
Hypothesis Testing
Hypothesis Testing
A procedure for deciding whether to accept or reject a statement (hypothesis) about a population based on sample data.
Precise statement of hypotheses
Precise statement of hypotheses
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Parametric Tests
Parametric Tests
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Non-Probability Sampling
Non-Probability Sampling
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Chi-square Test
Chi-square Test
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Exploratory/Formulative Research
Exploratory/Formulative Research
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Simple Random Sampling
Simple Random Sampling
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Analysis of Variance (ANOVA)
Analysis of Variance (ANOVA)
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Research Design
Research Design
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Convenience Sampling
Convenience Sampling
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Research Purpose Types
Research Purpose Types
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Judgement Sampling
Judgement Sampling
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Hypothesis
Hypothesis
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Flexible Research Design
Flexible Research Design
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Null Hypothesis
Null Hypothesis
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Deliberate Sampling
Deliberate Sampling
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Alternative Hypothesis
Alternative Hypothesis
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Minimizing Bias & Maximizing Reliability
Minimizing Bias & Maximizing Reliability
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Experimental vs Non-Experimental Designs
Experimental vs Non-Experimental Designs
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Type I Error
Type I Error
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Experimental Designs
Experimental Designs
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Type II Error
Type II Error
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p-value
p-value
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Study Notes
Hypothesis Testing
- Hypothesis: A tentative statement about a relationship or difference between variables, often based on prior knowledge and observations.
- Hypothesis Testing: A systematic process used in research to determine whether evidence from a sample supports or refutes a hypothesis about a population.
- Parametric Tests: Tests used for data that follows a normal distribution. These are used to assess means, differences between means, proportions, variances, and correlations.
- Hypothesis Testing of Means: Procedure to determine if a sample mean statistically differs from a hypothesized population mean.
- Hypothesis Testing for Differences between Means: Methods to determine if there's a significant difference between the means of two independent groups.
- Hypothesis Testing for Comparing Two Related Samples: Techniques to assess if there's a significant difference between paired samples, such as before-and-after measurements.
- Hypothesis Testing of Proportions: Methods to gauge if a sample proportion differs from a hypothesized population proportion.
- Hypothesis Testing for Difference between Proportions: Used to analyze the difference in proportions between two independent groups.
- Hypothesis Testing for Comparing a Variance to Some Hypothesized Population Variance: Procedures to test if a sample variance is significantly different from a specified population variance.
- Testing the Equality of Variances of Two Normal Populations: Methods to determine if the variances of two normal populations are equal.
- Hypothesis Testing of Correlation Coefficients: Tests used to assess the significance of a correlation between two variables.
- Limitations of Hypothesis Tests: Methods have limitations concerning factors like sample size, assumptions, and specific situations.
Chi-Square Test
- Chi-Square as a Test for Comparing Variance: Method for analyzing if observed variances differ significantly from expected variances.
- Chi-Square as a Non-parametric Test: Used for categorical data, assessing if observed frequencies differ from expected frequencies.
- Conditions for the Application of χ Test: Specifies when and how the Chi-Square Test can be used.
- Steps Involved in Applying Chi-Square Test: A step-by-step procedure for performing the test.
- Alternative Formula: Alternative mathematical representation of the test.
- Yates' Correction: Adjustment for small expected frequencies within contingency tables.
- Conversion of χ into Phi Coefficient: Transforming chi-square into a correlation-like coefficient.
- Conversion of χ into Coefficient by Contingency: Conversion to contingency coefficient.
- Important Characteristics of χ Test: Highlights key features of the Chi-Square Test.
- Caution in Using χ Test: Potential pitfalls or issues when applying and interpreting the results.
Analysis of Variance and Covariance
- Analysis of Variance (ANOVA): Statistical method for analyzing the differences among the means of more than two groups.
- What is ANOVA?: Details explaining ANOVA
- The Basic Principle of ANOVA: The underlying logic and justification of ANOVA.
- ANOVA Technique: The methodology behind the procedure.
- Setting up Analysis of Variance Table: Process of creating the analysis table to present results.
- Short-cut Method (One-way ANOVA): An efficient approach for one-way ANOVA.
- Coding Method: Coding techniques within ANOVA.
- Two-way ANOVA: Methods for analyzing the effects of two-factors on the study variable.
- ANOVA in Latin-Square Design: ANOVA applying to Latin-square experimental design.
- Analysis of Covariance (ANOCOVA): Extension of ANOVA to include the effect of a covariate.
- ANOCOVA Technique: Explained method of the procedure.
- Assumptions in ANOCOVA: Conditions of ANOCOVA.
Research Design and Sampling
- Working Hypotheses: Hypotheses developed before the study, based on prior knowledge and evidence.
- Research Design: A framework that guides the research, ensuring efficiency and relevance.
- Research Purposes: Classification of research into exploration, description, diagnosis, and experimentation.
- Research Designs: Various approaches, including experimental and non-experimental methods.
- Experimental Designs: Different types of experimental designs, like informal and formal.
- Sample Designs: Methodologies for selecting a representative subgroup of a larger group for research.
- Probability Samples: Samples where every member of the population has a known probability of selection.
- Non-probability Samples: Samples where the probability of selection isn't known for each member of the population.
- Deliberate Sampling: Selection of specific elements based on researcher's judgment.
- Convenience Sampling: Choosing units based on ease of access.
- Judgement Sampling: Selection relying on the researcher's expertise.
- Simple Random Sampling: Every member has an equal chance of selection.
- Other sampling methods (potentially): Stratified, cluster, systematic sampling methods.
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