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Stats variables
Understanding Variables and Data Scales in Psychology Research
| Question | Answer |
|---|---|
| Quasi-experimental methods | A study in which an independent variable is manipulated but people are not randomly selected or assigned to groups. |
| Anchors | visual or verbal descriptions of each possible response |
| Continuous data | the numbers used to measure a quantity |
| Categorical data | The result of breaking down research participants' characteristics or responses into specific categories. |
| Nominal scale | a scale in which objects or individuals are assigned to categories |
| Ordinal scale | a scale of measurement in which the measurement categories form a rank order |
| Interval Scale | A quantitative measurement scale that has no "true zero," and in which the numerals represent equal intervals (distances) between levels (e.g., temperature in degrees). |
| Ratio scale | A measurement that has a natural, or absolute, zero and therefore allows the comparison of absolute magnitudes of the numbers |
| What are the two types of variables mentioned? | Independent variable and dependent variable. |
| What is an independent variable? | The variable that is manipulated or changed in an experiment. |
| What is a dependent variable? | The variable that is measured or observed in response to changes in the independent variable. |
| What is the difference between categorical and continuous data? | Categorical data represents categories or groups, while continuous data represents measurable quantities. |
| What is the significance of treating ordinal data as interval data? | It allows for the calculation of averages, although it may not always be appropriate. |
| Stacking the Deck/cherry-picking | Ignoring examples that disprove a point and listing only those that support the case. |
| Conflict of Interest | When a researcher feels compelled to produce the results that the client wants |
| HARKing | When researchers revise their hypothesis and data analysis after the results of a study are known |
| Inappropriate outcome data | The misuse, misinterpretation, or manipulation of results from studies or data collection,to present a false or skewed conclusion. |
| Population | The larger group that a researcher is interested in studying, and from which the researcher needs to collect data. |
| Sample | a smaller set of the population intended to represent the population of interest |
| Represenative | The sample is demographically similar to the population of interest |
| Variable | a condition or characteristic that can have different values/change, or vary |
| What do the relative frequencies have to add up to? | 1 or 100% |
| What should the last cumulative frequency be in a frequency table? | the total sum of all frequencies (the total number of observations or data points in the set) |
| What visualization is appropriate for qualitative (categorical) data | bar charts, pie charts |
| What visualization is appropriate for quantitative data | histograms, density plots, and violin plots |
| How do pie charts and bar graphs relate to each other? | pie charts illustrate proportional relationships (parts of a whole, usually as percentages), while bar graphs highlight the absolute magnitude and direct comparison between independent categories. |
| Difference between bar graph and histogram | Bar graphs compare distinct categories using separated bars, while histograms display the frequency distribution of continuous numerical data using adjacent, touching bars |
| How do histograms, density plots, and violin plots relate to each other | All used to visualize the distribution and density of numerical data, with each offering increasing levels of smoothing and detail |
| What is the bin width in a histogram? | the size or range of each interval (bin) on the horizontal axis |
| Measures of central tendency | mean, median, mode |
| Measures of spread | Range, Interquartile Range, Standard Deviation, and variance |
| How are the mean and median affected by outliers/extreme values? | The mean is heavily influenced by outliers because it incorporates every value, causing extreme values to pull it up or down, while the median is generally unaffected, but can be |
| Why do we square each difference score to calculate variance? | It ensures all deviations from the mean are positive, preventing negative and positive differences from cancelling each other out |
| Why do we take the square root of the variance to get the standard deviation? | To return the measure of spread back into the original units of the data |
| Why does the formula for standard deviation involve n-1 in the denominator rather than n? | To provide an unbiased estimator of the true population variance |
| IQR (interquartile range) | order data from least to greatest, find the median to split the data into lower and upper halves, and identify the median of the lower half (π1) and upper half (π3), and calculate πΌππ =π3βπ1. The IQR represents the middle 50% of the data. |
| What's the mode of qualitative data? | the category, label, or response that appears most frequently in a dataset |
| How are the range and IQR affected by outliers/extreme values? | The range is highly sensitive to outliers. Conversely, the Interquartile Range (IQR) is resistant to outliers because it only measures the spread of the middle 50% of the data (π3βπ1), ignoring extreme values |
| How are outliers shown on a boxplot? | Outliers appear as individual points (dots, stars, or asterisks) located beyond the whiskers, which are the lines extending from the box |
| The median is the same as which percentile on a boxplot? | The 50th percentile |
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skyfalls
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