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Research Methods
| Term | Definition |
|---|---|
| Hypothesis | Testable prediction that lets us accept, reject, or revise a theory |
| Theory | An explanation based on evidence that predicts behaviors or events |
| Qualifications for a theory (4) | Must: fit the known facts, predict new discoveries, be falsifiable, and simple |
| How to develop a theory | surf and describe the worlds with the descriptive research, form a hypothesis from your observations, test her hypothesis, read test your hypothesis --> theory |
| Descriptive research purpose | To describe what is in reality |
| Descriptive research strengths and weaknesses | Strengths- can be quick and able to generalize. Weaknesses- can't help you predict, can't give you cause and effect |
| Three types of descriptive research | Naturalistic observation, survey, and case studies |
| Naturalistic observation | Researcher describes the behavior of the human or animal in their natural setting |
| Naturalistic observation strengths and weaknesses | Strengths- behavior is more natural. Weaknesses- cannot replicate, cannot generalize, and observer bias |
| Observer bias | when observers and know the goals of the study or hypotheses and allow the smallest influence their observations during the study |
| Case studies | Study of a single individual or just a few individuals in order to describe their situation by gathering as much evidence as you can |
| Case studies strengths and weaknesses | Strengths- takes advantage of a non-reportable situations, and gets a lot of in-depth understanding. Weaknesses- observer bias, cannot generalize, and cannot replicate |
| Survey | Questionnaires or interviews are given to selected group of people |
| Research methods | Descriptive (done), correlational, experimental |
| Correlational research purpose | To show relationship between two variables |
| Correlational research strengths and weaknesses | Strength- relationship can predict outcomes. Weakness- correlation is not causation |
| Experimental research purpose | Swiss tablets cause and effect relationships between variables |
| Experimental research strengths and weaknesses | Strength- ability to find out if one variable (IV) causes a change in another variable (DV). Weaknesses- confounding variables and experimenter bias |
| Operational definitions | statement of procedures the researcher is going to use in order to measure a specific variable. |
| Random sampling | Every person from a population has an equal chance of being selected for your study |
| Scatter plot graph | Comprised of plants generated by values of two variables. Slope of points depicts the direction. Amount of scatter shows the strength of relationship |
| Correlation coefficient | A statistical measure of relationship between two variables. When one trait or behavior varies with another we say the two correlate |
| 3rd or missing variable problem | a relationship other than cause my exists between the two variables. It's possible that there is some other variable or factor that is causing the outcome. You don't know this because you never controls for those variables |
| True relationships which can be mistaken for causation | Common response and confounding |
| Common response | Both x and y respond to changes in some unobserved to variable, z |
| Confounding | x and y respond to changes in some unobserved variables, a and b |
| Independent variable | Cause- the variable that is manipulated by the experimenter |
| Dependent variable | Effect- the variable that is measured by the experimenter that depends on the independent variable |
| Experimental group | In a controlled experiment, the group subjected to a change in the independent variable |
| Control group | In a controlled experiment, this is the group not subjected to a change in the independent variable |
| Placebo effect | What happens when a person takes a medication that he or she thinks will help, and therefore it actually does |
| Single blind procedure | During an experiment only the participant is unaware of the group there in, either the control or experimental group |
| Double blind procedure | During an experiment both participated and the researcher in the room are unaware of the group they are in <-- best |
| Confounding variables | Variables that a researcher fails to control for or eliminate |
| Experimenter bias | Errors in a research study due to the predisposed in ocean's our beliefs of the experimenter |
| Statistical reasoning | Statistical procedures analyze and interpret data and let us see what the unaided eye misses |
| Central tendency | Tendency of scores to congregate around some middle variable. Central tendency identifies what is average or typical in a data set |
| Measures of central tendency | Mode: most common. Mean: average. Median: middle number in a ranked order distribution |
| Skewed distributions | The tail tells the tale (for the direction of the skew) |
| How to measure average in skewed distribution | Using the median |
| Variance | Statistical dispersion - how distributed of the data points are |
| Measuring statistical dispersion | Range (highest # - lowest #) and standard deviation |
| Standard deviation | Measure of dispersion- average difference between the values |
| Normal distribution | Below -3SD: 0.1%, below -2SD: 2.1%, below -1SD: 13.6%, above -1SD: 34.1%. 1SD: 34.1%, above 1SD: 13.6%, above 2SD: 2.1%, above 3SD: 0.1%. |
| Inferential statistics | Trying to reach conclusions that extend beyond just described in the data |
| How to infer causation | Ask: is there a difference between the means of the two groups or did it just happen by chance? |
| How to test for differences | Run a t-Test |
| T test results | T test gives a p-value that allows us a measure of confidence in The observed a difference. A p-value of less than 0.05 what is a common criteria for significance (95% chance that differences are caused by IV, 5% chance caused by luck) |
Created by:
ebab
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