Psychology Reliability Concepts
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Questions and Answers

What is confirmability primarily concerned with in qualitative research?

  • The researchers' biases affecting results (correct)
  • The emotional response of the participants
  • The statistical significance of findings
  • The accuracy of participant experiences (correct)

Which qualitative approach focuses on analyzing experiences from the first-person perspective?

  • Phenomenology (correct)
  • Ethnography
  • Content analysis
  • Case study

What does participant observation involve?

  • Joining the group being studied (correct)
  • Interviewing participants about their experiences
  • Conducting surveys on public opinion
  • Analyzing data from existing literature

Which type of case study is completed before a larger exploratory study?

<p>Exploratory case study (D)</p> Signup and view all the answers

What is a key consideration when conducting a case study?

<p>Misleading single cases can lead to inaccurate generalizations (A)</p> Signup and view all the answers

Ethnography primarily looks at which aspects within a research context?

<p>Participant behavior and cultural identity (A)</p> Signup and view all the answers

Which external factors can significantly influence individual experiences?

<p>Gender, temporal, and societal contexts (D)</p> Signup and view all the answers

What does attrition refer to in a research study?

<p>The loss of participants before the study concludes (C)</p> Signup and view all the answers

What is a key characteristic of parametric tests?

<p>They assume specific properties about the underlying data distribution. (B)</p> Signup and view all the answers

What does the assumption of linearity ensure in Pearson's correlation?

<p>A straight line can describe the relationship between independent and dependent variables. (B)</p> Signup and view all the answers

In the context of assumptions for statistical tests, what does homoscedasticity refer to?

<p>Data shows consistent variability across the range of values. (B)</p> Signup and view all the answers

Which of the following correctly describes heteroscedasticity?

<p>Data shows unequal variability across the range of values. (C)</p> Signup and view all the answers

What is the assumption of normality crucial for in statistical analyses?

<p>It allows for valid parametric statistical methods. (A)</p> Signup and view all the answers

Which statistical method is an example of a non-parametric test?

<p>Spearman's correlation (A)</p> Signup and view all the answers

What type of designs are commonly seen in quasi-experimental designs?

<p>One group designs and nonequivalent group designs (B)</p> Signup and view all the answers

Why is the independence of errors important in regression analysis?

<p>It allows for meaningful predictions across the entire scale. (C)</p> Signup and view all the answers

What is the main advantage of sampling without replacement?

<p>It is the most common approach for large populations. (C)</p> Signup and view all the answers

How does stratified random sampling help in research?

<p>It allows for equal representation of different subgroups. (A)</p> Signup and view all the answers

What is a major drawback of cluster sampling?

<p>It may leave unselected clusters completely unrepresented. (A)</p> Signup and view all the answers

What can bias systematic sampling?

<p>Unforeseen patterns in the data. (D)</p> Signup and view all the answers

In sampling with replacement, what happens to the probability of selecting each individual?

<p>It remains constant across selections. (C)</p> Signup and view all the answers

What characterizes systematic sampling?

<p>A random starting point is selected, followed by fixed intervals. (D)</p> Signup and view all the answers

Which type of sampling reduces the risk of over- or under-representing specific groups?

<p>Stratified random sampling (C)</p> Signup and view all the answers

What can be a consequence of sampling error?

<p>The population mean may not be accurately reflected. (B)</p> Signup and view all the answers

What is a challenge posed by cluster sampling?

<p>Clusters may vary greatly in size. (C)</p> Signup and view all the answers

What is the effect of sampling without replacement in small populations?

<p>It significantly alters selection probabilities rapidly. (B)</p> Signup and view all the answers

What is the primary purpose of manipulating independent variables in experimental studies?

<p>To determine cause-effect relationships (A)</p> Signup and view all the answers

In a pre-post design research study, what is measured?

<p>The effects of an intervention before and after it occurs (A)</p> Signup and view all the answers

What distinguishes a control group in an experimental study?

<p>It helps assess the effects of the independent variable (A)</p> Signup and view all the answers

Which statistical tests are commonly used to measure group differences in experimental designs?

<p>t-tests and ANOVA (A)</p> Signup and view all the answers

What is a characteristic of quasi-experimental designs?

<p>They lack a control group and manipulation of independent variables (C)</p> Signup and view all the answers

How does internal validity differ among research designs?

<p>Higher in experimental designs due to better control over conditions (C)</p> Signup and view all the answers

What is a feature of non-experimental designs?

<p>Lack intervention from the researcher (A)</p> Signup and view all the answers

What is a limitation inherent in non-experimental designs?

<p>They cannot establish cause-effect relationships (C)</p> Signup and view all the answers

In what situation would a researcher use a longitudinal study?

<p>To observe changes in a variable over a long period (A)</p> Signup and view all the answers

What is meant by population validity in the context of external validity?

<p>The extent to which results apply to the general population (C)</p> Signup and view all the answers

Which of the following describes a key feature of cross-sectional studies?

<p>They collect data from different groups at one time (A)</p> Signup and view all the answers

Which of the following can be considered a quasi-independent variable?

<p>A pre-existing variable like age or gender (C)</p> Signup and view all the answers

Which type of research design typically allows for the most generalizability?

<p>Non-experimental designs with fewer constraints (B)</p> Signup and view all the answers

What does selective survival refer to in historical records?

<p>The process of excluding certain records which may lead to bias (D)</p> Signup and view all the answers

Which type of survey item allows respondents to provide answers in their own words?

<p>Open-ended items (A)</p> Signup and view all the answers

What is a characteristic of restricted items in surveys?

<p>They provide multiple choice answers. (A)</p> Signup and view all the answers

What defines a negative correlation between two variables?

<p>One variable increases while the other decreases (C)</p> Signup and view all the answers

In correlational research, what does a correlation coefficient of 0.85 indicate?

<p>A strong positive relationship (A)</p> Signup and view all the answers

What does the coefficient of determination (R2) signify in regression analysis?

<p>The proportion of variance in one variable explained by another (C)</p> Signup and view all the answers

What is a common reason for participant fatigue in surveys?

<p>Long length and repetitive questions (A)</p> Signup and view all the answers

Which type of survey question allows for some restricted options while permitting open responses if needed?

<p>Partially open-ended items (C)</p> Signup and view all the answers

What is the primary focus of psychometrics?

<p>Developing measures and assessments in psychology (B)</p> Signup and view all the answers

What does a regression line represent in statistical analysis?

<p>The expected relationship between two variables (B)</p> Signup and view all the answers

Which of the following correlations indicates a weak negative relationship?

<p>r = -0.20 (C)</p> Signup and view all the answers

What indication does Pearson’s r provide in a bivariate correlation?

<p>The strength and direction of a linear relationship (B)</p> Signup and view all the answers

What result would an R2 value of 0.3 suggest about a regression analysis?

<p>Little variance in the dependent variable explained (A)</p> Signup and view all the answers

Which design allows for pre-treatment baseline measurement but lacks a control group?

<p>Single group pre-post design (D)</p> Signup and view all the answers

What is a limitation of longitudinal studies?

<p>They can have high levels of attrition. (D)</p> Signup and view all the answers

Which approach involves measuring a dependent variable at multiple time points but lacks a control group?

<p>Basic time series (A)</p> Signup and view all the answers

In developmental designs, which type involves grouping participants by their age?

<p>Cross-sectional study (C)</p> Signup and view all the answers

In non-equivalent group designs, what is a primary issue that affects research outcomes?

<p>Selection differences (D)</p> Signup and view all the answers

Which design allows participants to act as their own control?

<p>Single case designs (B)</p> Signup and view all the answers

What feature distinguishes control time series from other time series designs?

<p>Inclusion of a control group (C)</p> Signup and view all the answers

Which analysis technique is used to assess significant differences between the means of groups?

<p>T-tests &amp; ANOVA (B)</p> Signup and view all the answers

In cohort-sequential designs, what advantage is provided over traditional longitudinal studies?

<p>Comparison of multiple cohorts simultaneously (A)</p> Signup and view all the answers

What type of design is characterized by alternating between treatment and baseline phases?

<p>Reversal design (A)</p> Signup and view all the answers

What does the effect size indicate in hypothesis testing?

<p>The magnitude of the difference between groups (D)</p> Signup and view all the answers

What limitation is common in interrupted time series designs?

<p>Lacks a control group for comparison (A)</p> Signup and view all the answers

What is the definition of autocorrelation in time series analysis?

<p>Correlation of a time series with its own past or future values (C)</p> Signup and view all the answers

Which design is often affected by cohort effects when comparing age groups?

<p>Cross-sectional studies (D)</p> Signup and view all the answers

Flashcards

Simple Random Sampling

Participants are chosen so every person in the population has an equal chance of being selected.

Sampling with Replacement

Each participant selected is put back into the population before the next selection. This maintains equal selection chances each time.

Sampling without Replacement

Participants are not replaced after selection, altering the selection chance on the next pick. This is most common.

Stratified Random Sampling

Dividing the population into subgroups (strata) then selecting participants from each using simple random sampling.

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Systematic Sampling

Selecting a participant randomly at a starting point. Then, choosing every other person thereafter.

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Cluster Sampling

Picking pre-defined groups (clusters) and selecting some representing the whole.

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Sampling error

Differences between sample statistics and population parameters. Happens in all sample studies.

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Sampling limitations

Systematic sampling's sensitivity to pattern & starting point, and cluster sampling's dependence on equal cluster size are limitations..

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Effect of population size

The change in selection probability from sampling with/without replacement is decreased in larger populations.

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Replacement

Returning the selected items to the population before the next selection

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Independent Variable (IV)

The variable that is manipulated in an experiment to observe its effect on another variable.

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Dependent Variable (DV)

The variable that is measured to see the effect of the manipulation of the independent variable.

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Pre-Post Design

A research design that measures a variable before and after an intervention.

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Randomization

Assigning participants to groups randomly to avoid bias.

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Control Group

A group that does not receive the treatment in an experiment.

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Quasi-Independent Variable

A variable that cannot be randomly assigned by the researcher.

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Within-Groups Design

Measuring the same participants over different time points.

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Between-Groups Design

Comparing different groups of participants.

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Internal Validity

The extent to which a study is free from confounding variables and demonstrates cause-and-effect accurately.

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External Validity

The extent to which findings can be generalized to other populations, settings, and times.

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Population validity

The degree to which the sample represents the entire population.

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Ecological Validity

How well the study findings apply to real-world situations.

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Non-experimental Design

Research approach that observes variables without manipulating them.

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Correlation

A statistical measure of the relationship between two variables.

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Meta-analysis

Combining results of multiple studies to reach broader conclusions.

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Confirmability

How well research results reflect participants' experiences, not the researcher's bias.

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Attrition

Participants dropping out of a research study before completion.

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Phenomenology

Qualitative study of conscious experiences from the participant's perspective.

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Ethnography

Qualitative study of a group's behaviour and identity, described by group members.

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Participant Observation

Researchers join the group or culture they are studying.

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Case Study

In-depth study of an individual, group, or organization to understand a phenomenon.

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Illustrative Case Study

Case study of a unique individual or situation where little is known.

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Collective Case Study

Examining similarities and differences in multiple related cases.

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Linearity Assumption

Assumes a straight-line relationship between your independent and dependent variables, crucial for linear regression and Pearson's correlation.

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Normality Assumption

Assumes your data follows a bell-shaped distribution, like a normal curve.

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Homoscedasticity

Means your data has consistent spread or variance across all values. Think of the 'spread' being the same throughout.

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Heteroscedasticity

The opposite of homoscedasticity: your data has uneven spread or changing variance across different values.

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Independence of Errors

Means the errors between observed values and predicted values shouldn't be related to each other.

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Non-parametric Test

A statistical method that doesn't require strict assumptions about data distribution.

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Spearman's Correlation

A non-parametric test that measures the strength of monotonic relationships between variables.

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Monotonic Relationship

A relationship where as one variable increases, the other consistently increases or decreases, but not necessarily at a constant rate.

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Selective Survival

The process where historical records persist or disappear over time, leading to bias in the historical record.

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Selective Depository

The process where records are selectively kept or discarded at the time of creation, resulting in bias in the historical record.

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Psychometrics

The study of developing and evaluating psychological tests and measures.

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Validation Study

Research conducted to assess the reliability and validity of a new measurement tool or refine existing measures.

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Open-ended Item

A survey question that allows respondents to answer freely in their own words.

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Partially Open-ended Item

A survey question with restricted options but a catch-all 'other' option for unique answers.

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Restricted Item

A survey question with a fixed set of answer options that respondents must choose from.

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Response Set

The tendency for respondents to answer consistently in the same direction across survey items, even if their true opinions differ.

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Reverse Coded Item

A survey item worded opposite to others, requiring reversed scoring for consistency.

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Participant Fatigue

Decreased effort and accuracy in survey responses due to factors like length or repetition.

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Theoretical Generalization

Applying survey findings to support an existing theory.

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Empirical Generalization

Extending survey findings to other similar populations or settings.

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Correlation Coefficient

A statistical measure that describes the strength and direction of the linear relationship between two variables.

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Correlation Strength

The magnitude of the correlation coefficient, indicating how strong or weak the relationship is.

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Regression

A statistical method used to predict the value of one variable based on the value of another variable.

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Single Group Post-Test Only Design

Research design where data is collected only after an intervention, without a control group or baseline measurement.

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Single Group Pre-Post Design

Data is measured before and after an intervention, but lacks a control group.

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Non-Equivalent Group Designs

Control group matched on pre-existing characteristics, but intervention occurs for one group only.

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Non-Equivalent Post-Test Only Design

Control group is not assessed before treatment, making it difficult to determine if differences are due to treatment or pre-existing factors.

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Non-Equivalent Pre-Post Design

Combines non-equivalent groups with pre-treatment measurement. Allows for comparison before intervention, but control group may differ substantially.

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Basic Time Series Design

Data is collected repeatedly at regular intervals, allowing for observation of trends and patterns over time. No control group.

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Interrupted Time Series Design

Measures the dependent variable before and after a naturally occurring event (like a disaster), allowing for analysis of change.

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Control Time Series Design

Time-series design with a control group, allowing for comparison with the treatment group.

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Longitudinal Studies

Tracks the same participants over time, measuring the same variable at different points. Useful for studying development.

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Cross-Sectional Study

Different age groups are studied at a single point in time.

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Cohort Effects

When differences between age groups are not due to age itself, but to the specific experiences of those groups.

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Cohort-Sequential Designs

Combines longitudinal and cross-sectional studies, observing multiple age groups across time.

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Reversal Design

Participants switch between a baseline condition (A) and a treatment condition (B) repeatedly to observe the effects of the intervention.

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Multiple Baseline Design

Treatment is applied to different participants or behaviors at different times, allowing for individual observation without a return to baseline.

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Changing-Criterion Design

Treatment is changed progressively based on the participant's performance, gradually increasing the desired behavior.

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Study Notes

Reliability

  • Reliability refers to the consistency, stability, or repeatability of a measure or observation.
  • Three types of reliability exist: test-retest, internal consistency, and inter-rater.

Test-retest Reliability

  • Measures the consistency of a measure over time.
  • A good test-retest reliability results in similar scores when the same test is administered repeatedly under the same conditions.
  • A poor test-retest reliability results in different scores when the same test is administered repeatedly under the same conditions.
  • Reliability is assessed by calculating the correlation between scores.
  • A strong correlation indicates good reliability, whereas a weak correlation indicates poor reliability.

Internal Consistency

  • Measures the extent to which multiple items or measures of a variable are related to each other.
  • Cronbach's alpha is a common measure of internal consistency.
  • Values range from 0 to 1. A higher value suggests better internal consistency.

Inter-rater Reliability

  • Used when measures are subjective and based on observer judgment, like evaluating behaviours.
  • Assesses the agreement between multiple raters assessing the same behaviour.
  • Cohen's kappa is a common measure of inter-rater reliability.
  • Scores from 0 to 1, higher scores indicate stronger agreement between raters.

Validity

  • Validity refers to whether a measure is actually measuring what it is supposed to be measuring.
  • Four types of validity are frequently discussed: face, construct, criterion-related, and content.

Face Validity

  • Refers to whether a measure appears to measure the variable it is designed to measure.
  • Subjective and relatively weak form of validity.

Construct Validity

  • Focuses on whether the measure accurately represents the underlying concept or variable.
  • Does the operational definition of a concept measure the relevant concept.
  • Examples include university entrance exams.
  • Measures how well a measure predicts or is related to a specific outcome or behavior.
  • A language proficiency exam is a good example - scores accurately predict future success in a language course.

Content Validity

  • The extent of how completely a measure represents the content domain it is designed to measure.
  • A comprehensive measure has comprehensive content.
  • Does the measure include all important components of the content domain and is nothing omitted, e.g., a test of General Knowledge.

Sampling

  • Target population: the entire group of interest to the researcher.
  • Accessible population: the portion of the target population that can be easily sampled.
  • Probability sampling: methods where every member of the population has a known chance of being selected. Includes simple random sampling, stratified random sampling, systematic sampling, and cluster sampling.
  • Non-probability sampling: methods where not every member of the population has a known chance of being selected. Includes convenience sampling and quota sampling.

Sampling Error

  • The difference between the sample statistic (e.g., mean, proportion) and the true population parameter
  • Standard error of the mean (SEM) measures the variability of sample means from the true population mean.
  • Larger samples typically result in lower sampling error.

Sampling Bias

  • When the sample doesn’t representative of the population.
  • This is common with non-probability sampling, when individuals are chosen based on accessibility e.g., student groups.

Research Questions

  • There are three broad categories of research questions:
  • Exploratory: to simply observe something or describe an outcome without testing hypotheses.
  • Descriptive: to describe the characteristics or a sample without testing hypotheses.
  • Relational: relating different variables, e.g. cause and effect.

Analyzing Experimental Data

  • Methods used to compare and quantify differences between groups in experimental studies.
  • T-tests are used to compare means or averages between two groups
  • ANOVA is used to compare means or averages between three or more groups.

Quasi-Experimental Designs

  • Quasi-experimental designs lack the complete control over factors that true experimental designs exhibit.
  • Cannot determine cause and effect relationships.

Qualitative Studies

  • Holistic understanding: a comprehensive and all encompassing grasp of complex phenomena,
  • Big data: large and complex datasets that require advanced tools.
  • Thick data: qualitative, context-rich information that provides deeper insights into human behavior—often collected through ethnography, interviews, and narratives.
  • Trustworthiness: The assessment tools for credibility, transferability, dependability, and confirmability used to assess the quality of qualitative studies.

Types of Qualitative Studies

  • Phenomenology.
  • Ethnography.
  • Case Studies.

Measurement in Surveys

  • Open-ended items
  • Partially open-ended items
  • Restricted items

Correlation

  • Correlation coefficient: a statistical measure of the strength and direction of the linear relationship between two variables (Pearson's r). Ranges from -1 to +1.
  • Direction (positive vs. negative) reflects how the variables move together.
  • Strength reflects the magnitude of the linear relationship.
  • Values close to +1 or −1 indicate strong relationships whereas values close to zero indicate weak or no linear relationships,
  • Does not indicate causation.
  • Linear regression can use the correlation coefficients to create a line of best fit for analysis.

Regression

  • Regression describes the relationship between dependent and independent variables.
  • Regression lines fit the data and can be used for prediction.
  • Coefficient determination (r2): measures the proportion of variance in one variable that can be explained by the variance in the other variable.

Types of Correlation

  • Positive correlation: as one variable increases, the other tends to increase.
  • Negative correlation: as one variable increases, the other tends to decrease.
  • There can be no correlation; as one variable changes, the other one does not change.

Regression Assumptions

  • Linearity: assumes a linear relationship between the variables.
  • Normality: assumes that the residuals (differences between observed and predicted values) are normally distributed.
  • Homoscedasticity: assumes that the variability of the dependent variable is constant across the range of the independent variable.

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Test #2 Study Notes PDF

Description

Explore the fundamental concepts of reliability in psychology, including test-retest reliability and internal consistency. Understand how these measures ensure the accuracy and consistency of observations. Dive into the significance of Cronbach's alpha and correlation in assessing reliability.

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