2022 Introduction to Statistics in Research Mitchell 2nd ed

I N T R O T O R E S E A R C H : D A T A V I S U A L I Z A T I O N & C O M M O N S T A T T E S T S

of a variable will approximate a normal distribution regardless of the distribution of the variable in the population. Essentially the CLT implies that even if the data distribution is not normal, the distribution of the means of the samples drawn from it will be normal! There is at least four applications of why knowing the means are normally distributed is essential: 1) able to assign confidence intervals; 2) perform t-tests for the difference between two samples; 3) performANOVA if the difference between means of three or more samples; and 4) perform any statistical test which uses sample mean. Chi-square statistic: is used to measure the agreement between categorical data. For example, you conduct a survey using two groups: management and employees (these are categories). The chi-square statistics compare the size of any discrepancy and whether it is significantly different. Cluster sample: Cluster sampling divides the population of interest into non-overlapping subgroups, called clusters. Clusters are then selected at random, and all individuals in the selected clusters are included in the sample.

Confidence interval : the combination of the interval range and the degree of confidence creates the confidence interval. Researchers typically use 5% confidence interval.

Confounding variables: two or more variables that are confounded when their effect on a response variable cannot be distinguished from each other.

Cohen’s d : An effect size test. Use it when t wo groups have similar standard deviations (SDs) and the same size sample

Continuous numerical: Variable that can be any value in a given interval. Usually measurements of something (like height).

Convenience sample: nonprobability sample in which element selection is based on ease of accessibility.

Correlation: a statistical measure (expressed in a number between -1 and +1) that describes the size and direction of a linear relationship between two or more variables. A correlation does not automatically mean that the change in one variable is the cause of the change in the value of the other variable. The best example in a general sense is smoking causes an increased risk of lung cancer and smoking correlates to alcoholism, but it does not cause alcoholism. Two variables can be strongly correlated without having any causal relationship, and two variables can have a causal relationship and yet be uncorrelated.

Correlation coefficient: is a measure of how nearly a scatterplot falls on a straight line (between -1 and +1).

Correlational hypothesis: a statement indicating that the variables occur together in some specified manner without implying that one causes the other.

Cronbach’s alpha α: this is the name of a test used to conduct reliability analysis on a questionnaire. Researchers use SPSS to assess internal consistency of scale by computing intercorrelations among the responses. A value of .70 or higher is acceptable internal consistency. If values are much lower, you should drop the question. This is usually done with a pilot questionnaire.

Cross-sectional study: compares different individuals to each other at the same time – a cross-section of a population. As an aside, there are lots of cross-sectional studies conducted because of COVID-19.

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