Analyzing and interpreting quantitative data

Copyright carter mcnamara, mba, phd, authenticity consulting,Adapted from the field guide to nonprofit program design, evaluation and field guide to consulting and organizational ns of this topic ing and interpreting should carry out the research? Of a research report -- an pitfalls to l information and and conducting d library see the library's blogs related to analyzing research addition to the articles on this current page, see the following blogs posts related to analyzing research results. Also see the section "recent blog posts" in r of the blog or click on "next" near the bottom of a post ing and interpreting ing quantitative and qualitative data is often the advanced research and evaluation methods courses. However,There are certain basics which can help to make sense of start with your research analyzing data (whether from questionnaires, interviews,Focus groups, or whatever), always start from review of your , i.

Collect analyze and interpret data

This will help you organize your data and focus your example, if you wanted to improve a program by strengths and weaknesses, you can organize data into ths, weaknesses and suggestions to improve the you wanted to fully understand how your program works, organize data in the chronological order in which clients go through your program. If you are conducting a ement study, you can categorize data according to each ated with each overall performance result, e. Ratings, rankings,Make copies of your data and store the master copy the copy for making edits, cutting and pasting, te the information, i. Then a research expert helps the determine what the research methods should be, and how ing data will be analyzed and reported back to the an organization can afford any outside help at all, it for identifying the appropriate research methods and how can be collected.

Conduct interviews, and analyze results of questionnaires, no outside help can be obtained, the organization can a great deal by applying the methods and analyzing lves. However, there is a strong chance that data strengths and weaknesses of a product, service or not be interpreted fairly if the data are analyzed by responsible for ensuring the product, service or a good one. Evaluation goals (eg, what questions are being of data/information that were data/information were collected (what instruments data/information were tions of the evaluation (eg, cautions about findings/ how to use the findings/conclusions, etc. Instruments used to collect data/, eg, in tabular format, onials, comments made by users of the product/service/ studies of users of the product/service/ related pitfalls to 't balk at research because it seems far too "scientific.

Nable ng/learning & strategic management library, © copyright authenticity consulting, llc ® ; all rights pell institute and pathways to college , organize, & clean unit of e quantitative e qualitative ces & icate & e quantitative tative data analysis is helpful in evaluation because it provides quantifiable and easy to understand results. In this section, you will learn about the most common quantitative analysis procedures that are used in small program evaluation. You will also be provided with a list of helpful resources that will assist you in your own evaluative tative analysis in you begin your analysis, you must identify the level of measurement associated with the quantitative data. There are four levels of measurement:Nominal data – data has no logical; data is basic classification e: male or is no order associated with male nor category is assigned an arbitrary value (male = 0, female = 1).

Data – data has a logical order, but the differences between values are not e: t-shirt size (small, medium, large). Data – data is continuous and has a logical order, data has standardized differences between values, but no natural e: fahrenheit er that ratios are meaningless for interval cannot say, for example, that one day is twice as hot as another e: items measured on a likert scale – rank your satisfaction on scale of 1-5. Very data – data is continuous, ordered, has standardized differences between values, and a natural e: height, weight, age, an absolute zero enables you to meaningful say that one measure is twice as long as example – 10 inches is twice as long as 5 ratio hold true regardless of which scale the object is being measured in (e. You have identified your levels of measurement, you can begin using some of the quantitative data analysis procedures outlined below.

Due to sample size restrictions, the types of quantitative methods at your disposal are limited. However, there are several procedures you can use to determine what narrative your data is telling. And advanced analytical demonstrate each procedure we will use the example summer program student survey data presented in “enter, organize, & clean data” tabulationdescriptivesdisaggregating the datamoderate and advanced analytical first thing you should do with your data is tabulate your results for the different variables in your data set. This process will give you a comprehensive picture of what your data looks like and assist you in identifying patterns.

Will help you determine:If scores are entered  scores are high or many are in each spread of the the table, you can see that 15 of the students surveyed who participated in the summer program reported being satisfied with the le frequencies for student summer program survey data. From the table, you can see that 75% of students (n = 20) surveyed who participated in the summer program reported being satisfied with the le percentages for student summer program survey data. The most common descriptives used are:Mean – the numerical average of scores for a particular m and maximum values – the highest and lowest value for a particular – the numerical middle point or score that cuts the distribution in half for a particular g the scores in order and counting the number of the number of scores is odd, the median is the number that splits the the number of scores is even, calculate the mean of the middle two – the most common number score or value for a particular ing on the level of measurement, you may not be able to run descriptives for all variables in your dataset. Tabulating the data, you can continue to explore the data by disaggregating it across different variables and subcategories of variables.

Crosstabs allow you to disaggregate the data across multiple data from our example, let’s explore the participant demographics (gender and ethnicity) within each program city. By looking at the table below, you can clearly see that the demographic makeup of each program city is abs – gender and ethnicity by program the table above, you can see that:Females are overrepresented in the new york program, and males are overrepresented in the boston 70% of the white sample is in the boston program while only 14% of the black sample is represented in that and latino/a participants are evenly distributed across both program entire native american sample (n=2) is the boston can also disaggregate the data by subcategories within a variable. On a 4-point scale) and that 75% of the students sampled were satisfied with their addition to the basic methods described above there are a variety of more complicated analytical procedures that you can perform with your data. 2017 the pell institute for the study of opportunity in higher education, the institute for higher education policy, and pathways to college pell institute and pathways to college , organize, & clean unit of e quantitative e qualitative ces & icate & e quantitative tative data analysis is helpful in evaluation because it provides quantifiable and easy to understand results.

Please view this video please enable javascript, and consider upgrading to a web browser r 6: analyzing and interpreting quantitative hed byclara ed about 1 year ad r tation on theme: "chapter 6: analyzing and interpreting quantitative data"— presentation transcript:Chapter 6: analyzing and interpreting quantitative dataeducational research: planning, conducting, and evaluating quantitative and qualitative research edition 5 john w. The end of this chapter, you should be able to:identify the steps in the process of analyzing and interpreting quantitative data describe the process of preparing your data for analysis identify the procedures for analyzing your data learn how to report the results of analyzing your data describe how to interpret the in the process of quantitative data analysispreparing the data for analysis conducting the data analysis reporting the results interpreting the ing the data for analysis: scoring the datascore data by assigning numeric codes to responses continuous scale example: score “strongly agree” as a “5” and “strongly disagree” as a “1. Categorical scale example: score “female” as a “1” and “male” as a “2” create a codebook using information from instruments, when ine types of scores to analyzesingle item summed scores difference ing a statistical programstatistical package for social sciences (spss) most popular other programs minitab jmp systat and account for missing dataidentify scores outside of the accepted range (errors) participants provide scores outside the range input mistakes assess the database for missing data and determine how to ting descriptive analysismeasures of central tendency (value or score that represents the entire distribution) mean: typically called the “average” median: the value or score that divides the top half of a distribution from the bottom half mode: the value or score that occurs most ting descriptive analysis (cont’d)measures of variability (describes the “spread” of the scores range: the difference between the highest and lowest scores standard deviation: the standard distance the scores are away from the ting descriptive analysis (cont’d)measures of relative standing percentile rank: the percentage of participants in the distribution with scores at or below a particular score calculated score: enables a researcher to compare scores from different scales z-score: a popular form of the standard score, has a mean of 0 and a standard deviation of ptive statisticscentral tendency variability relative standing mean median mode variance standard deviation range z-score percentile ntial statisticsanalysis of variance chi-square pearson correlation multiple regression ting inferential analysishypothesis testing: a procedure for making decisions about results by comparing an observed value of a sample with a population value to determine if no difference or relationship exists between the values confidence interval: the range of upper and lower statistical values that is consistent with observed data and is likely to contain the actual population ting inferential analysis (cont’d)effect size: a means for identifying the practical strength of the conclusions about group differences or about the relationship among ting hypothesis testsidentify a null and alternative hypothesis set the level of significance (alpha level) for rejecting the null hypothesis collect the data compute the sample statistic make a decision about rejecting or failing to reject the ing an appropriate statisticdetermine the type of quantitative research question or hypothesis you want to analyze (e. Two-tailed es of hypothesis testing: type i and type ii errorsdecision made by the researcher based on the statistical test value state of affairs in the population no effect: null true effect exists: null false type i error (false positive) (probability = alpha) correctly rejected: no error (probability = power) reject the null hypothesis correctly not rejected: no error type ii error (false negative) (probability = beta) fail to reject the null ing the results tables summarize statistical informationtitle each table present one table for each statistical test organize data into rows and columns with simple and clear headings report notes that qualify, explain, or provide additional information in the tables.

Notes include information about the sample size, the probability values used in hypothesis testing, and the actual significance levels of the statistical ing the results (cont’d)figures (charts, pictures, drawings) portray variables and their relationships labeled with a clear title that includes the number of the figure augment rather than duplicate the text convey only essential facts omit visually distracting detail easy to read and understand consistent with and are prepared in the same style as similar figures in the same article carefully planned and ing the results (cont’d)present results in detail report whether the hypothesis test was significant or not provide important information about the statistical test, given the statistics include language typically used in reporting statistical sing the resultssummarize major results review major conclusions to each question or hypothesis explain the implications of the results for the audiences explain why they occurred advance limitations suggest future research end on positive ad ppt "chapter 6: analyzing and interpreting quantitative data". Slide 1 chapter 8 analyzing and interpreting ing and interpreting quantitative r eight: using statistics to answer ix i a refresher on some statistical terms and ional research: competencies for analysis and application, 9 th edition. Mertler appendix overview of statistical concepts and ional research descriptive statistics chapter th edition chapter th edition gay and tative data tanding research results: statistical inference © 2012 the mcgraw-hill companies, 306, nursing research lisa broughton, msn, rn, ccrn research 2300: marketing research paul tilley unit 10: basic data ng acquainted with statistical concepts chapter chapter ional research: data analysis and interpretation – 1 descriptive statistics edu 8603 educational research richard m. Scientists analyze data collected in an experiment to look for patterns or relationships broad purposes of quantitative research 1.