SOCI 3201 - Exam 2

Calculating Cross Tabulation Values

Row percentages OR Column percentages OR Total percentages. Check 1: Decide which data set you are comparing. Check 2: Divide individual quantity by sum. Check 3: Compare only row OR column OR total based off independent variable.

Interpreting Cross Tabulation

Row percentages OR Column percentages OR Total percentages. Check 1: Where is the independent variable (column or row)? Check 2: Read percents, not numbers. Check 3: When converting to verbal, use proper terminology; "larger than, smaller than, less than, greater than, most, less, etc."

Longitudinal Research

A longitudinal study is an observational research method in which data is gathered for the same subjects repeatedly over a period of time. Longitudinal research projects can extend over years or even decades. In a longitudinal cohort study, the same individuals are observed over the study period.

Advantages of Secondary Data (Slides)

1. Nonreactive Measurement 2. Analyzing Social Structure 3. Understanding the Past 4. Understanding Social Change 5. Studying Problems Cross-Culturally 6. Replication and Increased Sample Size 7. Savings on Research Costs

Element

A data element is a unit of data for which the definition, identification, representation, and permissible values are specified by means of a set of attributes.

Standard Deviation

A measure that is used to quantify the amount of variation or dispersion of a set of data values. A low standard deviation indicates that the data points tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the data points are spread out over a wider range of values. Square root of variance. Expressed in the same units as the data.

Linear Relationship

A relationship of direct proportionality that, when plotted on a graph, traces a straight line. In linear relationships, any given change in an independent variable will always produce a corresponding change in the dependent variable.

Descriptive Statistics

A set of brief descriptive coefficients that summarizes a given data set, which can either be a representation of the entire population or a sample. The measures used to describe the data set are measures of central tendency and measures of variability or dispersion.

Mean

All values/# of values

Descriptive Statistics (Slides)

Central Tendency: Mode Median Mean Measure of Variation: Range Outlier Variance Standard Deviation Skewness

Frequency Tables

Display the number and percentage for cases corresponding to each variable's values or group of values. Should Include: 1. A title that clearly labels variables 2. Row labels that specify the values of a variable 3. Column labels that specify frequencies and percentages

Population

Entire group the sample is chosen from.

Curvilinear Relationship

In curvilinear relationships, the data points increase together up to a certain point (like a positive relationship) and then as one increases, the other decreases (negative relationship) or vice versa. On a scatterplot, this develops an arch in which the data increase together up to a certain point.

Cross Sectional Research

In medical research and social science, a cross-sectional study (also known as a cross-sectional analysis, transversal study, prevalence study) is a type of observational study that involves the analysis of data collected from a population, or a representative subset, at one specific point in time—that is, cross-sectional data.

Advantages of Secondary Data

It has been contended that the approach can be used to generate new knowledge, new hypotheses, or support for existing theories; that it reduces the burden placed on respondents by negating the need to recruit further subjects; and that it allows wider use of data from rare or inaccessible respondents. 1. It is economical. It saves efforts and expenses. 2. It is time saving. 3. It helps to make primary data collection more specific since with the help of secondary data, we are able to make out what are the gaps and deficiencies and what additional information needs to be collected. 4. It helps to improve the understanding of the problem. 5. It provides a basis for comparison for the data that is collected by the researcher.

Probability Sampling

Keys: (1) representative sample and (2) random selection. 4 types of PS - 1. Simple Random Sample 2. Systematic Sampling: Select every "kth" case from a complete list of the population. 3. Stratified Random Sampling: The population is first subdivided into two or more mutually exclusive segments, called strata, based on one or a combination of relevant variables. THEN, take a simple random sample of each group. 4. Cluster Sampling: Elements are selected in two or more stages. First, you select some clusters that are naturally formed in the population. Then, draw cases from selected clusters.

Variance

Measures how far a set of numbers are spread out. A variance of zero indicates that all the values are identical. Variance is always non-negative: a small variance indicates that the data points tend to be very close to the mean (expected value) and hence to each other, while a high variance indicates that the data points are very spread out around the mean and from each other.

Mode

Most occurring number.

Median

Numerically sorted from least to greatest, then find middle integer

Types of Sampling

Probability: All cases in the population are randomly selected and have a known probability of being included in the sample. the goal is to obtain a sample of cases that it representative of the population as a whole. Non-probability: Cases are not randomly selected. So, the chances of selecting any case are not known. For example, judgmental, quota, and snowball sampling.

Secondary Data Sets

Secondary analysis involves the use of existing data, collected for the purposes of a prior study, in order to pursue a research interest which is distinct from that of the original work; this may be a new research question or an alternative perspective on the original question.

Correlational Analysis

Statistical technique that summarizes the strength of a relationship between two quantitative variables.

Non-probability Sampling

Term is used to refer to all sampling methods that do not fulfill the criteria of probability sampling. 4 types of NPS - 1. Convenience Sampling: also known as "availability sampling," "haphazard sampling," or "accidental sampling." 2. Purposive Sampling: The researcher relies on his or her expert judgment to select units that are "representative" or "typical" of the population. 3. Quota Sampling: Quota sampling is a form of purposive sampling that bears a mild resemblance to stratified random sampling. As in stratified random sampling, quota sampling also begins by dividing the population into relevant strata such as age, gender, race or geographic region. At the second stage, the researcher uses convenience sampling to select respondents. 4. Referral/Snowball Sampling: Sample elements are selected as they are identified by successive informants or interviewees.

Central Tendency

The most common value (for nominal measures) or the value around which other cases tend to cluster (for qualitative measures).

Correlation Coefficient

Varies from -1 to 1. -1 indicates a perfectly negative relationship between two variables; +1 indicates a perfectly positive relationship between two variables; and 0 means there is no relationship between the two variables.