Hypothesis Testing Basics

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Questions and Answers

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.

True (A)

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 __________.

<p>Experimentation</p>
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Match the types of designs with their appropriate characteristics:

<p>Completely randomized design = Control over external variables Randomized block design = Eliminates bias Latin square design = Controls for two variables Factorial design = Examines interaction effects</p>
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What is a characteristic of exploratory research designs?

<p>They are flexible to consider various aspects of a problem. (B)</p>
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What type of design is best for minimizing bias and maximizing reliability when describing a situation?

<p>Descriptive design</p>
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Experimental designs are exclusively formal and do not include informal designs.

<p>False (B)</p>
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What is the primary purpose of analyzing interaction effects in factorial designs?

<p>To assess how one independent variable influences the effect of another (A)</p>
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In a factorial design, interaction effects can exist between more than two factors.

<p>True (A)</p>
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What is a two-way ANOVA used for?

<p>To analyze the impact of two independent variables on one dependent variable.</p>
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Factorial designs are characterized by the manipulations of ____ or more independent variables.

<p>two</p>
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Match the following types of factorial designs with their descriptions:

<p>Full Factorial Design = Tests all possible combinations of factors Fractional Factorial Design = Tests only a subset of combinations Nested Factorial Design = One factor is nested within another Split-Plot Design = Used when there are two levels of experimental units</p>
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Which of the following statistical tests is commonly used to evaluate the significance of interaction effects in a factorial design?

<p>ANOVA (D)</p>
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In a factorial design, higher-order interactions are always of primary interest.

<p>False (B)</p>
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What is a key assumption made in ANOVA regarding the groups being compared?

<p>The groups should have equal variances.</p>
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Which sampling method ensures that every item in the population has an equal chance of being included in the sample?

<p>Simple random sampling (D)</p>
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Judgment sampling allows for the inclusion of a random selection of participants from the population.

<p>False (B)</p>
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What is the primary feature of convenience sampling?

<p>Ease of access to population elements</p>
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In ______ sampling, researchers use their judgment to select items believed to be representative of the population.

<p>judgment</p>
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Match the following sampling methods with their descriptions:

<p>Simple random sampling = Each item has an equal chance of inclusion Convenience sampling = Selection based on ease of access Judgment sampling = Selection based on researcher’s judgment Deliberate sampling = Purposive selection of specific units</p>
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Which of the following is NOT a type of probability sampling?

<p>Judgment sampling (B)</p>
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Stratified sampling divides the population into homogeneous groups before sampling.

<p>True (A)</p>
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Name one advantage of using simple random sampling.

<p>Reduces bias in selection</p>
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Flashcards

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

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

A procedure for deciding whether to accept or reject a statement (hypothesis) about a population based on sample data.

Precise statement of hypotheses

Working hypotheses are most useful when clearly defined and stated; this ensures the research is targeted and testable.

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Parametric Tests

Statistical tests that assume specific characteristics about the data's distribution

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Non-Probability Sampling

Sampling methods where the chance of an item being selected is not known, often relying on convenience or the researcher's judgement, including convenience, judgment, and quota sampling.

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Chi-square Test

A statistical test used to analyze categorical data. It assesses if there's a significant association between two categorical variables.

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Exploratory/Formulative Research

Research that doesn't require a focused hypothesis. Its primary goal is to uncover insights and understand a problem rather than test a specific idea.

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Simple Random Sampling

Every possible item in the population has an equal chance of being selected for the sample. Typical methods include using random number tables or a lottery.

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Analysis of Variance (ANOVA)

A statistical method used to compare means across multiple groups, determining whether the observed differences are statistically significant or due to random chance

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Research Design

A framework that outlines how the research will be conducted, including the collection and analysis of data. It ensures efficiency and minimizes unnecessary effort.

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Convenience Sampling

Selecting participants based on ease of access; those most readily available are chosen.

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Research Purpose Types

Research can aim to explore, describe, diagnose, or experiment. Different purposes call for varying research strategies.

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Judgement Sampling

Selecting participants based on the researcher's judgment of who is representative of the population.

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Hypothesis

A testable statement or claim about a population parameter.

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Flexible Research Design

Suitable for exploratory research, allowing for adaptation and adjustments as more insights are gained during the study.

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Null Hypothesis

The default assumption in hypothesis testing. It states that there's no significant relationship or effect between variables.

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Deliberate Sampling

A type of non-probability sampling where specific elements are chosen to represent the population.

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Alternative Hypothesis

The hypothesis that states that there is a significant relationship or effect

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Minimizing Bias & Maximizing Reliability

Essential factors in research design when the purpose is descriptive or seeks to establish association between variables. This ensures accurate and reliable data.

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Experimental vs Non-Experimental Designs

Different approaches to conducting research, one typically involves manipulating variables, while the other observes variables.

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Type I Error

Rejecting a null hypothesis when it is actually true.

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Experimental Designs

Designs utilizing controlled experiments, to test the effect of variables on outcomes, and be further classified into formal designs (e.g., randomized block design) and informal designs (e.g., before-and-after study).

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Type II Error

Failing to reject a null hypothesis when it is actually false.

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p-value

The probability of observing a result as extreme as, or more extreme than, the one obtained, assuming the null hypothesis is true.

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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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