After data collection, the researcher must prepare the data to be analyzed. Organizing the data correctly can save a lot of time and prevent mistakes. Most researchers choose to use a database or statistical analysis program (e.g. Microsoft Excel, SPSS) that they can format to fit their needs and organize their data effectively. Once the data has been entered, it is crucial that the researcher check the data for accuracy. This can be accomplished by spot-checking a random assortment of participant data groups, but this method is not as effective as re-entering the data a second time and searching for discrepancies. This method is particularly easy to do when using numerical data because the researcher can simply use the database program to sum the columns of the spreadsheet and then look for differences in the totals. One of the best methods of checking for accuracy is to use a specialized computer program that cross-checks double-entered data for discrepancies.(1)

Descriptive statistics describe but do not draw conclusions about the data. Each descriptive statistic summarizes multiple discrete data points using a single number. They can tell the researcher the central tendency of the variable, meaning the average score of a participant on a given study measure. The researcher can also determine the distribution of scores on a given study measure, or the range in which scores appear. Additionally, descriptive statistics can be used to tell the researcher the frequency with which certain responses or scores arise on a given study measure. For example, in the Module 1 example about the effectiveness of corrective lenses on economic productivity, the researcher might observe that the average dollars-per-week of a person with corrected vision is $500, whereas the average DPW for a person without corrected vision is $450. This amount of information is not enough information to conclude that vision correction affects economic productivity. Inferential statistics are necessary to draw conclusions of this kind. Descriptive statistics might also tell the researcher that the distribution of DPW is $351-$640 for the whole sample and that the average DPW is $445 for the sample.(2)

Inferential statistics allow the researcher to begin making inferences about the hypothesis based on the data collected. This means that, while applying inferential statistics to data, the researcher is coming to conclusions about the population at large. Inferential statistics seek to generalize beyond the data in the study to find patterns that ostensibly exist in the target population. This course will not address the specific types of inferential statistics available to the researcher, but a succinct and very useful summary of them, complete with step-by-step examples and helpful descriptions, is available here.(4)

(1) Trochim, W. M. K. “Data Preparation” *Research Methods Knowledge Base 2nd Edition.* Accessed 2/24/09.

(2) Trochim, W. M. K. “Descriptive Statistics” *Research Methods Knowledge Base 2nd Edition.* Accessed 2/24/09.

(3) Trochim, W. M. K. “Descriptive Statistics” *Research Methods Knowledge Base 2nd Edition.* Accessed 2/24/09.

(4) Trochim, W. M. K. “Inferential Statistics” *Research Methods Knowledge Base 2nd Edition.* Accessed 2/24/09.

(5) Pelham, B. W.; Blanton, H. *Conducting Research in Psychology: Measuring the Weight of Smoke, 3rd Edition.* Wadsworth Publishing (February 27, 2006).