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Questions and Answers
What is a variable?
What is a variable?
A variable is any attribute of objects, people, or events that, within the context of a particular investigation, can take on different values.
What is a constant?
What is a constant?
A constant is any attribute of objects, people, or events that, within the context of a particular investigation, has a fixed value.
Which of these are types of variables based on Measurement NOT Design?
Which of these are types of variables based on Measurement NOT Design?
Which measurement Levels are typically associated with Categorical (Qualitative Variables)?
Which measurement Levels are typically associated with Categorical (Qualitative Variables)?
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It is impossible to make conclusions on ratios when using an Interval level of measurement.
It is impossible to make conclusions on ratios when using an Interval level of measurement.
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The ______ variable is manipulated or changed by the experimenter.
The ______ variable is manipulated or changed by the experimenter.
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The ______ variable is measured under each condition / level of the independent variable.
The ______ variable is measured under each condition / level of the independent variable.
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What are the key steps involved in organizing data for analysis?
What are the key steps involved in organizing data for analysis?
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Statistics can be used to provide insights into the past and future.
Statistics can be used to provide insights into the past and future.
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What is the difference between descriptive and inferential statistics?
What is the difference between descriptive and inferential statistics?
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What are the three most commonly used measures of central tendency?
What are the three most commonly used measures of central tendency?
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What is the mode?
What is the mode?
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Match the measures of central tendency with their descriptions:
Match the measures of central tendency with their descriptions:
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What is the interquartile range (IQR)?
What is the interquartile range (IQR)?
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What is the mean absolute deviation?
What is the mean absolute deviation?
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What is the sum of squares?
What is the sum of squares?
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What is the standard deviation?
What is the standard deviation?
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The standard deviation is always greater than or equal to zero.
The standard deviation is always greater than or equal to zero.
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What are the characteristics of a distribution with positive kurtosis?
What are the characteristics of a distribution with positive kurtosis?
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What are the factors that influence the size of the standard error?
What are the factors that influence the size of the standard error?
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What are the characteristics of a normal distribution?
What are the characteristics of a normal distribution?
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The standard deviation of a sampling distribution is always smaller than the standard deviation of the population.
The standard deviation of a sampling distribution is always smaller than the standard deviation of the population.
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The size of the standard error is influenced by the variability of the scores, but not the sample size.
The size of the standard error is influenced by the variability of the scores, but not the sample size.
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What is the purpose of hypothesis testing?
What is the purpose of hypothesis testing?
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The alpha (α) level defines the percent of the most unlikely outcomes in a hypothesis test.
The alpha (α) level defines the percent of the most unlikely outcomes in a hypothesis test.
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What are the critical z-values when using a 95 percent confidence level?
What are the critical z-values when using a 95 percent confidence level?
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A score that falls within the 5% rejection region for a one-sided test will always fall within the rejection region for the corresponding two-sided test.
A score that falls within the 5% rejection region for a one-sided test will always fall within the rejection region for the corresponding two-sided test.
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What differentiates a one-sample z-test from other statistical tests?
What differentiates a one-sample z-test from other statistical tests?
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What are the three approaches to hypothesis testing?
What are the three approaches to hypothesis testing?
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The confidence interval approach is identical to the critical value approach, but the center of the interval is based on the sample mean rather than the population mean.
The confidence interval approach is identical to the critical value approach, but the center of the interval is based on the sample mean rather than the population mean.
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If a one-sample t-test is conducted and the confidence interval includes 0, then it is safe to conclude that there is no difference between the sample mean and the population mean.
If a one-sample t-test is conducted and the confidence interval includes 0, then it is safe to conclude that there is no difference between the sample mean and the population mean.
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What are the four possible outcomes of a hypothesis test?
What are the four possible outcomes of a hypothesis test?
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What is the difference between a Type I error and a Type II error?
What is the difference between a Type I error and a Type II error?
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What is the power of a test?
What is the power of a test?
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The power of a test is inversely proportional to the probability of a Type II error.
The power of a test is inversely proportional to the probability of a Type II error.
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The effect size is independent of the sample size.
The effect size is independent of the sample size.
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The power of a test can be increased by increasing the effect size or increasing the sample size.
The power of a test can be increased by increasing the effect size or increasing the sample size.
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What distinguishes a t-test from a z-test?
What distinguishes a t-test from a z-test?
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The degrees of freedom for a one-sample t-test is calculated as N-1.
The degrees of freedom for a one-sample t-test is calculated as N-1.
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The shape of the t-distribution is unaffected by the number of degrees of freedom.
The shape of the t-distribution is unaffected by the number of degrees of freedom.
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The z-table can be used to find the critical t-value for a one-sample t-test.
The z-table can be used to find the critical t-value for a one-sample t-test.
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What is the difference between an independent samples t-test and a paired samples t-test?
What is the difference between an independent samples t-test and a paired samples t-test?
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The standard error of the difference for an independent samples t-test is calculated by taking the square root of the sum of the variances of each group.
The standard error of the difference for an independent samples t-test is calculated by taking the square root of the sum of the variances of each group.
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The formula for calculating a t-statistic in an independent samples t-test is the same regardless of whether the population standard deviation is known or unknown.
The formula for calculating a t-statistic in an independent samples t-test is the same regardless of whether the population standard deviation is known or unknown.
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The degrees of freedom for an independent samples t-test is calculated as the sum of the degrees of freedom for each group minus 1.
The degrees of freedom for an independent samples t-test is calculated as the sum of the degrees of freedom for each group minus 1.
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The confidence interval for an independent samples t-test is constructed around the difference between the two sample means, and it is used to determine if the population means are equal.
The confidence interval for an independent samples t-test is constructed around the difference between the two sample means, and it is used to determine if the population means are equal.
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Study Notes
Course Introduction
- Course name: PSY 201, Introduction to Statistics for Psychology I
- Instructor: Dr. Nihan Albayrak-Aydemir
Research Methods in Psychology
- Focuses on research methodologies within psychology.
Understanding Data in Psychological Research
- A lecture on variables, measurement, and data organization in psychological research.
- Teaching Assistant: Saliha Erman, MA
- Adapted from Assoc. Prof. Güneş Ünal, Boğaziçi University, 2022
Variable
- A variable is any attribute of objects, people, or events that can take on different values within a particular investigation.
- Examples: height, reaction time, test scores, eye color.
- A constant is the opposite of a variable, having a fixed value within a particular investigation.
- An attribute can be a variable in one context and a constant in another.
Within-Subject vs. Between-Subject Variance
- Changes and variations can be seen within and between individuals.
- Example: Mood
Types of Variables
- Qualitative (categorical) variables take a value that is one of several possible categories.
- The values of qualitative variables are categories (though they sometimes can be represented by numbers, but these numbers serve as labels only).
- They express differences in kind, not amount.
- Examples: College attended, fruit types, nationality, blood type.
- Quantitative (continuous) variables are numerical variables such as height, decision time, GPA, age, or proportion of items correctly answered.
- The values of quantitative variables are numbers.
- They express differences in amount.
Levels of Measurement
- Nominal scale- a measurement scale used to categorize or label variables.
- It does not have any quantitative value or order.
- The categories on a nominal scale represent different groups or types.
- There is no inherent ranking or hierarchy among them.
- Examples: Left-handed, right-handed. Favorite ice cream flavour: vanilla, chocolate, strawberry, etc.
- Ordinal scale- has the properties of a nominal scale, but the observations can be ranked in order of magnitude.
- Example: Position finished in a race.
- Interval scale- has all the properties of an ordinal scale, and a given distance between measures has the same meaning anywhere on the scale.
- Also called an equal-interval scale.
- Degrees of temperature, calendars years.
- Ratio scale- has all the properties of an interval scale plus an absolute zero point.
- This allows for the comparison of ratios.
- Examples: length, weight, reaction time, dollars.
Caution on Ratio Scale
- On the interval level of measurement, you cannot make conclusions on ratios.
- Example: 20°C is not twice as hot than 10°C.
- Likert type scaling is considered an ordinal scale because the intervals between points are not necessarily equal.
Independent and Dependent Variables
- Independent variable (IV): The variable that is manipulated or changed by the experimenter.
- Dependent variable (DV): The variable that is measured under each condition or level of the independent variable.
- Also called explanatory variable or predictor variable.
- Expected to explain the changes in the dependent variable.
- Also called response variable or outcome variable.
Research Design: Natural and Manipulated Independent Variables
- Natural IVs: The experimenter does not have complete control.
- Manipulated IVs: The experimenter has complete control.
- Quasi Experiments: Observing a natural variable and comparing it to another variable.
- True Experiments: Observing a manipulated variable and comparing it with another variable.
Correlations Between Variables
- In some studies, there is no clear cause-and-effect relationship between the variables.
- i.e. the relationship between measures of depression and anxiety.
- In correlational designs, neither variable can be considered a predictor or outcome variable.
Data Organization
- Data entry: Entering raw data into a software tool.
- Data coding: Assigning numerical or categorical codes to responses.
- Cleaning data: Removing or correcting errors.
- Tools: Excel, SPSS, Jamovi
Data Organization (Properly organized data ensures...)
- Efficiency: Efficiency in the analysis process, saving time and effort.
- Accuracy: Reduces the risk of errors that could distort results
- Reproducibility: Well-organized data can be more easily verified or reanalyzed by other researchers
Descriptive Statistics and Inferential Statistics
- Procedures used to summarise, organize and make sense of a set of scores or observations.
- Procedures used to allow researchers to infer from or generalise observations made within smaller samples to the larger population.
Measures of Central Tendency
- Mean: Average of a set of scores
- Median: The middle score in a sorted set of scores
- Mode: Most frequent score
Measures of Variability
- Range: Highest - Lowest Score
- Interquartile range (IQR): Distance between the values of the third and the first quartiles.
- Mean absolute deviation (MAD): average of the absolute deviations from the mean.
- Sum of Squares (SS): sum of squared deviation scores.
- Variance (s²): Sum of Squares (SS) divided by the number of cases (N)
- Standard Deviation (s): the positive square root of the variance.
Sample vs. Population Notation
- Sample Variance (s²): Σ(Y - Y)² / N
- Population Variance (o²): Σ(Y - μ)² / N (μ — the population mean)
Boxplots
- A simple graphical representation of data that captures features of both the location and spread of scores in a distribution
Z-Scores
- Transformed scores to a common scale.
- Have a mean of zero (0) and a standard deviation of one (1).
- Allow the comparison of different distributions.
Confidence Interval (CI)
- A range of values that's used to estimate an unknown population parameter.
- It gives a certain degree of confidence that the true population parameter lies within the interval.
Hypothesis Testing
- A process to test an idea (or a theory) by using data collected from a sample of the population.
- Begin with a Null and Alternative Hypothesis.
- Set an alpha level.
- Perform an appropriate statistical test.
- Calculate the p-value from the test statistic.
- Compare the p-value to the alpha level to decide whether to reject the null hypothesis.
One Sample z-test
- A test used to compare the mean of a sample with the mean of a population, when the population's standard deviation (σ) is known.
One Sample t-test
- A test used to compare the mean of a sample with the mean of a population, when the population's standard deviation (σ) is not known or suspected to be different from the sample's standard deviation (s).
Independent Samples t-Test
- A test used to determine if there is a statistically significant difference between the means of two independent groups or samples.
- The groups are sampled from mutually exclusive subgroups (i.e. participants belong only to one group).
- The dependent variable is always quantitative or measured on an interval or ratio level.
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Description
Explore the foundational research methodologies used in psychological studies. This quiz covers topics such as variables, measurement, and data organization essential for understanding data in psychological research. Engage with the concepts introduced in PSY 201, Introduction to Statistics for Psychology I.
