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Chapter 5: Descriptive Research. Describe patterns of behavior, thoughts, and emotions among a group of individuals. Provide information about characteristics about the sample rather than to test hypotheses. 1) Survey : most common type of descriptive research.
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Chapter 5: Descriptive Research • Describe patterns of behavior, thoughts, and emotions among a group of individuals. • Provide information about characteristics about the sample rather than to test hypotheses. 1) Survey: most common type of descriptive research. • Select a sample of the population using predetermined questions • this allows you to describe attitudes of population from which sample was drawn • can compare attitudes of different populations and look for changes in attitudes over time.
Surveys are usually questionnaires or interviews • Cross-sectional survey: survey one more more samples of the population in a single group • allows researchers to describe characteristics of a population or the differences between two or more populations • Successive independent samples: a series of cross-section series is done. Survey two or more samples at different times with same questions. • Allows researcher to study changes in a population over time, but not how individuals change over time. • study people’s level of trust in the government at 1972 and 1980.
Problems with Successive independent samples: • People surveyed at one time are not the same people surveyed at the next time. • Noncomparable successive samples: successive samples may not representative of the same population (e.g. could have more poor people in the successive sample). • Can only describe changes in population over time if successive samples represent the same population. • ACT scores from 1998 through 2002 decreased because more students wrote the ACT in 2002 who had no intention of going to college.
Longitudinal or panel survey design: same respondents are surveyed over time • allows researchers to examine changes in individuals over time. • Because it is correlational it is difficult to determine the causes of changes over time • Attrition: people may drop out of study over time (move, death etc) and the final sample may not longer be comparable to the original sample. • Internet Surveys: can be advantageous • inexpensive, can contact people that are hard to reach otherwise, people can respond when they want • But, researcher has little control over who responds and people without internet cannot respond.
2) Demographic Research: • describes patterns of life events and experiences like birth, marriage, employment etc. 3) Epidemiological Research: • study the occurrence of disease in groups of people • Psychologists may study how diseases impact people’s behavior and lifestyle. • Also study the prevalence of psychological disorders.
Sampling • Selection of people (sample) to participate in research in order to make inferences about a larger group (population). Probability Sampling • each member in a population has a specific probability of being chosen. • representative sample: is approximately the same as the population in every respect. Researchers can draw unbiased and accurate estimates of the larger population.
Sampling error: differences between the sample and the population. Almost inevitable Error of estimation (margin of error): represents how much the data from the sample differ from the population. Estimation of the sampling error. • allows researchers to determine how confident they are that the sample results represent the population. • 60% of sample chose Tide, accurate within 4% points. • 95% probability the true % who chose tide is between 56% and 64%
Error of estimation is influenced by: Sample size: the larger the sample to more similar the sample is to the population. • Economical sample: a reasonable estimate of the population. Size of population: sample of 100 is more representative if population is 500 rather than 5000. Variability: the larger the variability, the more difficult it is to estimate population values, and the larger the sample must be.
Probability sample: sample for which the researchers knows the probability that an individual is included in the sample. • Epsem design (equal probability selection method): used so all individuals in population have an equal probability of being chosen.
Ways to obtain a probability sample: Simple random sampling: each person in the population has an equal chance of being selected. • Researcher must have a complete list (sampling frame) of all the people in the population. • Randomly select from the sample frame • If you want to select sample of 100 students from a school with 5000 students. • You would get list of all 5000 students and number each from 1 to 5000. • Use a table of random numbers to produce 100 numbers that fall between 1 and 5000 and select those 100 students to be in your sample.
Stratified random sampling: because there may be different subgroups within your population (age, gender, ethnicity) you may want to choose people from each subgroup (stratum). • Cases are randomly selected from each strata • ensures you have an adequate number of participants from each stratum. • Proportionate sampling: cases are drawn from each strata in proportion to their prevalence in the population. • If population is 70% men and 30% women, then sample proportionately to ensure 70% men in sample
Cluster Sampling: used if you cannot obtain a sampling frame list of the entire population • Difficult to get a list of all health care workers in Alberta. • Break the population into smaller groups (clusters) for which there are sampling frames and then randomly choose some of the clusters for inclusion the sample. • Clusters are usually locations or institutions • Randomly choose different counties in Alberta and then get a list of all workers in those counties • Multistage sampling: begin sampling large clusters, then smaller and smaller clusters
Sampling Problems • Nonrepsonse: if not all people respond to survey, then those that do respond may have different characteristics than those that did not. • If return rate is less that 100% the data may be biased in some way. • Researchers try to increase response rates with follow up calls • Try to determine whether respondents and nonrepsondents differ in particular ways. • Misgeneralization: occurs when a researcher generalizes to a population that differs from the sample.
Nonprobability Samples • Occurs when a researcher does not know the probability of their sample • In many cases it is very difficult to obtain a probability sample. • Much of psychological research in conducted on samples that are not representative whole population (e.g. undergrads, rats, kids in daycare). • This is OK because behavioral research usually describes how variables are related to each other to support a theory, regardless of the nature of the sample. • This is why replication (with different samples) is important to improve generalizability of findings.
Convenience sampling: using a sample of individuals that is readily available. • E.g. Schools nearby, autism clinic, undergrads. • Quota sampling: convenience sampling, but you ensure that you get particular proportions of certain individuals. • Half boys and half girls; 20 4-, 6-, and 8-year-olds • Purposive sampling: researcher decides which individuals are included to get a representative sample. • Snowball sampling: one or more individuals for a population are contacted, and these individuals are used to lead the researchers to other members.
Describing and Presenting Data Frequency Distributions • Table that indicates how many, and is some cases the %, of individuals in the sample that fall into different categories. • Simple frequency distribution: If using numbers (scores) possible scores are arranged from lowest to highest and then the frequency of each score is shown.
Frequency Table: Number of Movies Seen in the Last 6 Months Reported by Psychology Students
Grouped frequency distribution: combine values into a set of equal class intervals. • Relative frequency: proportion of the total number of scores that fall into each interval • class interval must be mutually exclusive • class intervals must encompass all scores • all class intervals should be the same size
Measures on Central Tendency Gives information about the distribution • Mean: average, most common • mean is easily influenced by outliers • Median: score in the center of the distribution • less influenced by outliers • Mode: most frequent score • can have more than one mode
Measures on Variability • Descriptive stats that provide information about about the dispersion of scores. • range • variance (s2) and standard deviation (s) • standard deviation represents variability using the original units, not squared units as in variance • most data roughly fall into the normal distribution • however, in skewed distributions most scores falls toward one end of the distribution
Z score: represents how one score is relative to the rest of the data. • Indicates how far the score is from the mean in standard deviations. • Z scores can help to identify outliers.
