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| Question | Answer |
| Interviews | Questions are read to the respondent in person or phone Can be structured or unstructured |
| Interview Advantages | Able to reach otherwise “unreachable” populations Increases respondent understanding Develop rapport Obtain detailed information about complex subjects Can follow-up to what they say |
| Interview Disadvantages | Costly Increased training of researcher Time consuming Landlines for phones Response bias because of social desirability |
| Questionnaires | A set of fixed-format, self-report items respondents complete at their own pace often without supervision Generally written |
| Questionnaire Advantages | Comparatively/Inexpensive Anonymity/Confidentiality |
| Questionnaire Disadvantages | *Lower response rates *Increased random error |
| Response Rate | Percentage of people who: complete a questionnaire and return it to the investigators If low = incorrect conclusions about research Systematic difference? If so, validity issues! |
| Techniques to increase response rate | Gift incentive Visually pleasing Confidentiality/anonymity Follow up |
| Sampling Terminology | Population Census Sampling Sample Sampling Frame Representative Sample |
| Population | The entire group of people the researcher desires to learn about |
| Census | Measures each person about whom we wish to know |
| Sampling | The selection of people to participate in a research project We use these people to make inferences about a larger group of individuals |
| Sample | The smaller group of people who actually participate in the research |
| Sampling Frame | A complete list of all of the people in the population |
| Representative Sample | Approximately the same as the population in every important respect |
| Ways to Sample | Whenever samples are used, the researcher will never be able to know exactly the true characteristics of the population Goal: get as close as you can so the data that you gain from the sample can be applied to the general population. |
| Probability sampling | Each person in the population has a known chance of being selected HAVE to have a sampling frame! |
| Simple random sampling | Each person in the population has an equal chance of being selected. Process: select people/objects from the sampling frame at random until you reach a desired amount of people/objects in your sample |
| Systematic random sampling | If the list of names on the sampling frame is known to be in a random sequence, every nth name can be selected Draw a number blindly to tell you where to start on the random list Population size/sample size you want = # |
| Stratified Sampling | Involves drawing separate samples from a set of known subgroups called strata rather than sampling from the population as a whole. This is done to be representative of various variables, such as age, gender, ethnicity, geographic region, etc. |
| Sampling Bias | Probability sampling (aka – “representative sampling”) assumes: The existence of one or more sampling frames listing the entire population of interest and Everyone has the potential to be sampled |
| Sampling Bias | Occurs when either of these conditions is not met There is the potential the sample is not representative of the population |
| Non-probability Sampling | - Use when probability sample are impossible or not necessary (e.g. – no sampling frame of the population). |
| Non-probability Sampling Snowball sampling | Rare or difficult to reach population One or more individuals from the population are contacted These individuals lead the researcher to other population members |
| Non-Probability Sampling Convenience/Volunteer samples | The researcher samples whatever individuals are readily available without any attempt to make the sample representative |
| Non-Probability Sampling Purposive Sampling (judgmental) | Based on: Researchers knowledge of the population Purpose of the study Sample selection is based on some unique characteristic of the people in the population that is essential to the study |
| I have the sample and collected my data. What do I do now??? | Raw Data |
| Raw Data | Data collected from each measured variable must be aggregated and transformed to be meaningfully interpreted techniques: Frequency distributions Descriptive statistics |
| Frequency Distributions | A table indicating how many individuals (and what %) in the sample fall into each of a set of categories Nominal (discreet) variables |
| Bar Chart | A visual display of a frequency distribution |
| Grouped Frequency Distributions | shows the frequency of a subset of scores by combining adjacent values into a set of categories Approach to summarizing quantitative variables (as well as continuous) |
| Histogram | A visual display of a grouped frequency distribution |
| Frequency Curve | Frequencies in a grouped frequency distribution are indicated with a line rather than bars Useful for trends over time |
| Descriptive Statistics | Numbers that summarize the pattern of scores observed on a measured variable This pattern is called the distribution of the variable A distribution can be described by: Central tendency and Dispersion (spread) |
| Central tendency | Summarized using the Mean Median Mode |
| Dispersion | Summarized using the Variance Standard deviation |
| Measures of Central Tendency The Mean -Arithmetic mean- | The most commonly used measure of central tendency Basically the “Average” = x or x; μ Can have only one mean per distribution influenced by extreme scores (called outliers) EX = 80, 95, 60, 76, 91 |
| Measures of Central Tendency The Median | The score in the center of the distribution Only one value per distribution Not influenced by extreme values Used when distributions are not “normal” (skewed) EX = 80, 95, 60, 76, 91 Rank order from lowest to highest Median will be the score in the middle |
| Measures of Central Tendency The Mode | The value that occurs most frequently in a distribution 1 or more modes Not influenced by extreme values Gives the least information in terms of description Ex: 1,1,2,3,4,4,4,8,9 Median? Mode? |
| Distribution | The pattern of scores observed on a measured variable Data distributions that are shaped like a bell are known as normal distributions “Non-normal” shapes are known as skewed |
| Shapes of Distributions | In a normal distribution, all three measures of central tendency fall at the same point on the distribution. |
| Skewed (NOT Normal) Distributions -Outliers- | Extreme scores in a distribution Result in distributions that are not symmetrical |
| Skewed | Distributions that are not symmetrical Can be either positively or negatively skewed |
| Shapes of Distributions | In a positively skewed distribution, the outliers are on the right side of the distribution. In a negatively skewed distribution, the outliers are on the left side of the distribution. |
| Measures of Dispersion Dispersion (‘the spread’) | Extent to which the scores are tightly clustered around or spread out away from the central tendency |
| Range | One simple measure of dispersion least useful measurement of dispersion Calculated as the maximum observed score minus the minimum observed score |
| The Standard Deviation | The most common measure of dispersion useful for describing how much the scores in a set of data vary. Symbolized as s and calculated by: Computing each score minus the mean of the variable (mean deviations) Squaring the mean deviations and summing them (sum of squares) Dividing the sum of squares by the sample size, N (variance or s2) Taking the square root of the variance (standard deviation or s) |
| Standard Deviation and the Normal Curve | Assuming a normal distribution, we can estimate the percentage of subjects who obtain certain scores just by knowing the mean and standard deviation of the data. The Empirical Rule: Approximately 68% of the scores will fall in the range defined as +/- 1 standard deviation Approximately 95% of the scores will fall between +/- 2 standard deviations from the mean. Approximately 99% of the scores will fall between +/- 3 standard deviations from the mean. |
| Sample Size and the Margin of Error | Ultimate purpose of descriptive stats: Make inferences about the population Because of random error, sample characteristics will most likely not be exactly the same as the population. Frequently known as the margin of error of the sample |
| Sample Size and the Margin of Error | To minimize Margin of Error: Increase the size of a sample (N) Makes it more likely the sample will be representative of the population Provides more precise estimates of population characteristics |
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