Examples of dependent variable in the following topics:
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- By creating a controlled environment, researchers can test the effects of an independent variable on a dependent variable or variables.
- The dependent variable, on the other hand, depends on the independent variable, and will change (or not) because of the independent variable.
- The dependent variable is the variable that we want to measure (as opposed to manipulate).
- Determining how the independent variable will be manipulated and how the dependent variable will be measured
- Control groups are used to determine if the independent variable actually affects the dependent variable.
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- Research has shown that alcohol dependence correlates with depression.
- Correlational statistics, however, measure the strength of the relationship between two variables, indicating that the two variables are connected and not unrelated.
- Despite measuring the strength of the relationship between two variables, correlational statistics do not prove that one variable causes the other to occur.
- As an example of a correlational study, research has shown that alcohol dependence correlates with depression.
- When the mean, median, and mode are unequal, the normal curve can become skewed in either a negative or positive direction depending on their values in relation to each other.
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- In this example, the mood induction condition is the independent (manipulated) variable, while participants' responses on the emotion survey is the dependent (measured) variable.
- The major issue with this method is its accuracy: since surveys depend on subjects' motivation, honesty, memory, and ability to respond, they are very susceptible to bias.
- Structured surveys, particularly those with closed-ended questions, may have low validity when researching affective variables.
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- How you remember an event depends on a large number of variables, including everything from how much sleep you got the night before to how happy you were during the event.
- Cue-dependent forgetting, also known as retrieval failure, is the failure to recall information in the absence of memory cues.
- State-dependent cues are governed by the state of mind at the time of encoding.
- Under cue-dependent forgetting theory, a memory might be forgotten until a person is in the same state.
- Context-dependent cues depend on the environment and situation.
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- These schedules are described as either fixed or variable and as either interval or ratio.
- Variable refers to when the number of responses or amount of time between reinforcements varies or changes.
- In a variable-ratio schedule, the number of responses needed for a reward varies.
- Extinction of a reinforced behavior occurs at some point after reinforcement stops, and the speed at which this happens depends on the reinforcement schedule.
- The variable-interval schedule is unpredictable and produces a moderate, steady response rate (e.g., fishing).
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- Individuals high in conscientiousness prefer planned rather than spontaneous behavior and are often organized, hardworking, and dependable.
- Critics of the trait approach argue that the patterns of variability over different situations are crucial to determining personality—that averaging over such situations to find an overarching "trait" masks critical differences among individuals.
- Factor analysis, the statistical method used to identify the dimensional structure of observed variables, lacks a universally recognized basis for choosing among solutions with different numbers of factors.
- A five-factor solution depends, on some degree, on the interpretation of the analyst.
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- Central tendency and variability measures are used to interpret the meaning and value of data.
- There are three variability measures of a data set: range, standard deviation, and variance.
- Another way to measure the variability of the data is through skewness.
- When the mean, median, and mode are unequal, the normal curve can become skewed in either a negative or positive direction depending on their values in relation to each other.
- Explain the descriptive statistics used to measure central tendency and variability
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- Research studies with small sample sizes, high variability, and sampling bias are usually not representative of the general population.
- A study's external validity can be threatened by such factors as small sample sizes, high variability, and sampling bias.
- Data sets with similar values are considered to have little variability because the values are within a smaller spread, whereas data sets with values that are spread out have high variability because the values are within a larger spread.
- The results of the surveys often depend on the city, state, or area being surveyed.
- One group should not show substantially higher characteristics of a given variable than the other, as this can distort the findings.
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- Although convenience samples are not as ideal as a random sample, they are easy to collect, and depending on the research question, may provide an good enough approximation of the population being studied.
- Accordingly, depending on several other factors, larger samples can also give the researcher a greater chance that their results will be statistically significant, meaning that it can increase their power to detect an effect.
- The reason the normal distribution is so important is because most inferential statistics are based on the assumption that the variable we are measuring is normally distributed.
- If our variable is normally distributed, that gives us confidence that if we were to obtain the whole population of observations for that variable, the resulting distribution would also be normally distributed, and therefore the inferences we draw (using statistics) are accurate.
- What type of inferential statistics we use will inevitably depend on our research question and our type of data.
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- Correlational studies are used to show the relationship between two variables.
- Unlike experimental studies, however, correlational studies can only show that two variables are related—they cannot determine causation (which variable causes a change in the other).
- A correlation of 0 indicates no relationship between the variables.
- Since there is no random assignment to conditions, a researcher cannot rule out the possibility that there is a third variable affecting the relationship between the two variables measured.
- Instead, the third variable of education level affects both.