1/14/2013. Professor David Long. The Scientific Method. Four major nonscientific sources of knowledge

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1 Professor David Long Four major nonscientific sources of knowledge Intuition: Quick and ready insight not based on rational thought Common sense: Refers to opinions widely held because they seem obvious Authority: Someone who supposedly has special knowledge that not everyone has Tradition: Knowledge because that is what those older than us state as fact Nonscientific knowledge often provides false or misleading information because it lacks the capacity for critique Science is based on principles of: Objectivity: Scientists are expected to prevent their personal biases from influencing the interpretation of their results, so that data are to be interpreted on the basis of merit Verifiability: A study can be repeated by others and allows critical analysis, retesting, and revision by colleagues Research is based on two forms of methodology Quantitative Research: Collects and reports data primarily in numerical form. Qualitative Research: Relies on what is seen in field and naturalistic settings; often focuses on small groups, subcultures, & communities. Bias: An unintentional error in a particular direction that may produce misleading or erroneous conclusions. Bias can occur because of: Expectations of subjects: Often they believe they know what the experimenter wants, so to accommodate them, they answer questions in a particular direction instead of being honest. Expectations of the experimenters: While experimenters are suppose to open-minded and neutral, sometimes they can believe in something so much that they may see something that really is not there, or may interpret some neutral behavior in a way that confirms their theory or hypothesis. The Scientific Method Defining the Research Question Before any type of research can begin, you need an idea of what is the problem and how it can be defined. This is often facilitated by theories, which are broadbased scientific frameworks that help to tentatively explain the causes of behavior. Theories are made up of sets of statements that help to explain a particular phenomenon. Theories help to: Organize data Provide direction 1

2 Literature Review: The most difficult part of any type of research. You want to know what other people have done. Hypotheses: Tentative predictions or educated guesses about the outcome of a study. Hypotheses are based on the literature review, and are more specific than theories. Can be described as the 'true' starting point of your research study. Operational Definitions: Once your hypotheses have been stated, you now describe exactly what variables you will be observing. Variable: Measurable trait or characteristic subject to change under different conditions Quantitative Variables: Variation that can be measured and given numerical value (e.g., individuals, groups, objects) Qualitative Variables: Variation in kind versus in numerical degree (e.g., sex, marital status) Percentage: Shows portion of 100 Mean: Average; sum of a series of values divided by the number of values ( ) / 7 = 4 Mode: Single most common value in a series of values 1, 2, 2, 3, 4, 7, 9 = 2 Median: Midpoint that divides a series of values into two groups with equal numbers of values 1, 2, 2, 3, 4, 7, 9 = 3 Independent Variable: Variable hypothesized to cause or influence another. The factors that are manipulated by the experimenter. The topic of research has nothing to do with their occurrence. Dependent Variable: Variable in which a change is expected to occur. The anticipated change depends on the influence of the independent variable. There may be several dependent variables. Dependent variables are often considered the scores or outcomes of a study. Control Variables: Factors held constant to test the impact of the Intervening Variables: Influences the relationship between an independent variable and dependent variable. Hawthorne Effect: Unintended influence of observers or experiments on subjects Research Design: Detailed plan or method for obtaining data scientifically Types of Research Designs: Experimental Correlational Survey Observational Archival or Meta-analysis Experimental & Control Groups: Randomly assign research participants to either the experimental group or the control group. The experimental group is the group of subjects that will be exposed to the The control group will not be exposed to the 2

3 Correlation: Exists when a change in one variable coincides with a change in another variable. Correlation does not necessarily indicate causation. Positive Correlation: Both the independent and dependent variable(s) change in the same direction Negative Correlation: Independent and dependent variable(s) change in opposite directions (when one goes up the other goes down) Spurious Correlation: A mathematical relationship in which two occurrences have no causal connection, yet it may be inferred that they do, due to a certain third, unseen factor (referred to as a "confounding factor"). The spurious relationship gives an impression of a significant link between two groups that is invalid when objectively examined Survey: Study that uses an instrument to provide sociologists with information about how people act or think Interview: Researcher obtains information through face-to-face or telephone questioning Questionnaire: Researcher uses printed or written form to obtain information from respondent Return rate is often low (around 30%). Your sample is no longer random because often only the same type of people (e.g., same economic status) return your questionnaire. Answers may be suspect. Person can not be truthful. It is extremely difficult or impossible to follow up on responses or delve deeper. Surveys are often used when a research wants to study a large number of people. Reliability: Accuracy, precision, or consistency of measurement. Degree to which measures are free from error and therefore yield consistent results Would two people understand a question in the same way? Would the same person give the same answers under similar circumstances? Reliable measures mean the same data would have been collected under similar circumstances Validity: Degree to which a study accurately reflects or assesses the specific concept that the researcher is attempting to measure While reliability is concerned with the accuracy of the actual measuring instrument or procedure, validity is concerned with the study's success at measuring what the researchers set out to measure Do the findings reflect the opinions, attitudes, and behaviors of the target population? Systematic study of behavior in which the researcher is immersed in the social setting of the subjects. Pros: You can see the subjects in natural settings, and this can lead to the development of theories or hypotheses. Cons: You cannot control any variables, thus you cannot draw cause-and-effect conclusions. The results you obtain are very descriptive in nature. Types of field studies: Participant Observation: Sociologist joins group to get accurate sense of how it operates Ethnography: Efforts to describe an entire social setting through extended systematic observation 3

4 Secondary Analysis: Research techniques that make use of previously collected and publicly accessible information, such as letters. Content Analysis: Systematic coding and objective recording of data, guided by some rationale Meta-Analysis: Combination of different researchers data sets. Population: After identifying your variables, you have to determine all of the members of the group that you want to study. This is the group of people that will ultimately be affected by your findings. The population you choose is usually too large to test everyone that comprises the group. Because it is impossible to measure everyone in your population, you must choose and measure a sample of the population. Sample: A subgroup of the population. How large samples should be? Large enough to be representative and small enough to be manageable. To ensure a representative sample, your sample must be selected randomly. Random Sample: Each member of your population has an equal chance of being selected (e.g., pick names out of a phone book or out of a hat). Convenience Sampling: Members of the population are chosen based on their relative ease of access. Systematic Sampling: In a population of n units and a desired sample of s units, the auditor selects every r th unit (n/s) systematically beginning at a random point among the first r units in the population. Using this procedure, each element in the population has a known and equal probability of selection. The researcher must ensure that the chosen sampling interval does not hide a pattern. Any pattern would threaten randomness. A random starting point must also be selected. Stratified Sampling: Method used to divide a population into homogeneous subgroups (strata), and each stratum is then sampled individually. Cluster Sampling: The population is subdivided into groups called clusters so that there is small variability within clusters and large variability between clusters. The first unit of each group is selected with the use of a random number table. This allows selection of more than one item at a time. Null Hypothesis: Describes in a formal way some aspect of the statistical behavior of a set of data and this description is treated as valid unless the actual behavior of the data contradicts this assumption. Probability: A way of expressing knowledge or belief that an event will occur or has occurred. Significance: A difference of such magnitude between two statistics, computed from separate samples, that the probability of the value obtained will not occur by chance alone with significant frequency and hence can be attributed to something other than chance. 4

5 In statistical hypothesis testing, the p-value is the probability of obtaining a result at least as extreme as the one that was actually observed, given that the null hypothesis is true. In modern investigation the generally accepted value for significance must have a probability of occurrence by chance factors equal to or less than five times in 100 (p < 0.05). Generally, one rejects the null hypothesis if the p-value is smaller than or equal to the significance level. If the level is 0.05, then the results are only 5% likely to be as extraordinary as just seen, given that the null hypothesis is true. 5