Here are 4 methods to deal with missing data. Step 3: Data Coding This is one of the most important steps in data preparation. It refers to grouping and assigning values to responses from the survey. For example, if a researcher has interviewed 1, people and now wants to find the average age of the respondents, the researcher will create age buckets and categorize the age of each of the respondent as per these codes. For example, respondents between years old would have their age coded as 0, as 1, as 2, etc.
Then during analysis, the researcher can deal with simplified age brackets, rather than a massive range of individual ages. Quantitative Data Analysis Methods After these steps, the data is ready for analysis. The two most commonly used quantitative data analysis methods are descriptive statistics and inferential statistics. Descriptive Statistics Typically descriptive statistics also known as descriptive analysis is the first level of analysis. It helps researchers summarize the data and find patterns.
A few commonly used descriptive statistics are: Mean: numerical average of a set of values. Median: midpoint of a set of numerical values. Mode: most common value among a set of values. Percentage: used to express how a value or group of respondents within the data relates to a larger group of respondents. Frequency: the number of times a value is found. Range: the highest and lowest value in a set of values. Descriptive statistics provide absolute numbers.
However, they do not explain the rationale or reasoning behind those numbers. For example, a percentage is a good way to show the gender distribution of respondents. Descriptive statistics are most helpful when the research is limited to the sample and does not need to be generalized to a larger population. For example, if you are comparing the percentage of children vaccinated in two different villages, then descriptive statistics is enough. Since descriptive analysis is mostly used for analyzing single variable, it is often called univariate analysis.
Inferential Statistics Often, researchers collect data on a sample of their population, then they generalize the results to the entire population or target group. Inferential statistics are used to generalize results and make predictions about a larger population. These are complex analyses that show the relationship between several different variables, rather than describing a single variable.
They are used when the researcher needs to go beyond absolute values and understand the relations between variables. A few types of inferential analysis are: Correlation: This describes the relationship between two variables.
If a correlation is found, it means that there is a relationship among the variables. For example, taller people tend to have a higher weight. Hence, height and weight are correlated with each other. Regression: This shows the relationship between two variables.
Analysis of variance: This is a statistical procedure used to test the degree to which two or more groups vary or differ in an experiment. In most experiments, a great deal of variance indicates that there was a significant finding from the research.
For example, to understand the relationship between the number of children in a family and the socio-economic status, a researcher may recruit a sample of families from each socio-economic status and ask them about their ideal number of children. The choice of inferential statistic completely depends upon the research objective. Like in the case of descriptive statistics, it is best to identify the appropriate inferential statistic for your research questions.
Since inferential statistics are used to determine the relationship between two or more variables, they are called bivariate analysis when limited to two variables or multivariate analysis when there are more than two variables.
The above-stated methods are the most commonly used methods for data analysis. However, other data analysis methods and metrics, such as standard deviation and variance, are also available. Analyzing Qualitative Data Qualitative data analysis works a little differently from quantitative data, primarily because qualitative data is made up of words, observations, images, and even symbols. Deriving absolute meaning from such data is nearly impossible; hence, it is mostly used for exploratory research.
While in quantitative research there is a clear distinction between the data preparation and data analysis stage, analysis for qualitative research often begins as soon as the data is available. Data Preparation and Basic Data Analysis Analysis and preparation happen in parallel and include the following steps: Getting familiar with the data: Since most qualitative data is just words, the researcher should start by reading the data several times to get familiar with it and start looking for basic observations or patterns.
This also includes transcribing the data. Revisiting research objectives: Here, the researcher revisits the research objective and identifies the questions that can be answered through the collected data. Developing a framework: Also known as coding or indexing, here the researcher identifies broad ideas, concepts, behaviors, or phrases and assigns codes to them. For example, coding age, gender, socio-economic status, and even concepts such as the positive or negative response to a question.
Coding is helpful in structuring and labeling the data. Identifying patterns and connections: Once the data is coded, the research can start identifying themes, looking for the most common responses to questions, identifying data or patterns that can answer research questions, and finding areas that can be explored further.
One way is to use word-based methods, such as word repetitions. The amount of information that a variable provides will become important in the analysis stage, because we lose information when variables are reduced or aggregated—a common practice that is not recommended. As the terms imply, the value of a dependent variable depends on the value of other variables, whereas the value of an independent variable does not rely on other variables. In addition, an investigator can influence the value of an independent variable, such as treatment-group assignment.
Independent variables are also referred to as predictors because we can use information from these variables to predict the value of a dependent variable. Building on the group of variables listed in the first paragraph of this section, blood glucose could be considered a dependent variable, because its value may depend on values of the independent variables age, sex, ethnicity, exercise frequency, weight, and treatment group. Statistics are mathematical formulae that are used to organize and interpret the information that is collected through variables.
There are 2 general categories of statistics, descriptive and inferential. Descriptive statistics are used to describe the collected information, such as the range of values, their average, and the most common category. Knowledge gained from descriptive statistics helps investigators learn more about the study sample. Inferential statistics are used to make comparisons and draw conclusions from the study data. Knowledge gained from inferential statistics allows investigators to make inferences and generalize beyond their study sample to other groups.
Before we move on to specific descriptive and inferential statistics, there are 2 more definitions to review. Parametric statistics are generally used when values in an interval-level or ratio-level variable are normally distributed i. These statistics are used because we can define parameters of the data, such as the centre and width of the normally distributed curve. In contrast, interval-level and ratio-level variables with values that are not normally distributed, as well as nominal-level and ordinal-level variables, are generally analyzed using nonparametric statistics.
This can be done using figures to give a visual presentation of the data and statistics to generate numeric descriptions of the data. Selection of an appropriate figure to represent a particular set of data depends on the measurement level of the variable. Data for nominal-level and ordinal-level variables may be interpreted using a pie graph or bar graph. Both options allow us to examine the relative number of participants within each category by reporting the percentages within each category , whereas a bar graph can also be used to examine absolute numbers.
For example, we could create a pie graph to illustrate the proportions of men and women in a study sample and a bar graph to illustrate the number of people who report exercising at each level of frequency never, sometimes, often, or always.
Interval-level and ratio-level variables may also be interpreted using a pie graph or bar graph; however, these types of variables often have too many categories for such graphs to provide meaningful information. Instead, these variables may be better interpreted using a histogram. Unlike a bar graph, which displays the frequency for each distinct category, a histogram displays the frequency within a range of continuous categories.
Information from this type of figure allows us to determine whether the data are normally distributed. In addition to pie graphs, bar graphs, and histograms, many other types of figures are available for the visual representation of data. Figures are also useful for visualizing comparisons between variables or between subgroups within a variable for example, the distribution of blood glucose according to sex.
Box plots are useful for summarizing information for a variable that does not follow a normal distribution. The lower and upper limits of the box identify the interquartile range or 25th and 75th percentiles , while the midline indicates the median value or 50th percentile.
Scatter plots provide information on how the categories for one continuous variable relate to categories in a second variable; they are often helpful in the analysis of correlations. In addition to using figures to present a visual description of the data, investigators can use statistics to provide a numeric description. Regardless of the measurement level, we can find the mode by identifying the most frequent category within a variable.
When summarizing nominal-level and ordinal-level variables, the simplest method is to report the proportion of participants within each category. The choice of the most appropriate descriptive statistic for interval-level and ratio-level variables will depend on how the values are distributed.
If the values are normally distributed, we can summarize the information using the parametric statistics of mean and standard deviation.
While in quantitative research there is a clear distinction between the data preparation and data analysis stage, analysis for qualitative research often begins as soon as the data is available. The choice of the most appropriate descriptive statistic for interval-level and ratio-level variables will depend on how the values are distributed. Descriptive statistics provide absolute numbers.
Scatter plots provide information on how the categories for one continuous variable relate to categories in a second variable; they are often helpful in the analysis of correlations. How to write data analysis in research proposal Additional information, such as the formulae for various inferential statistics, can be obtained from textbooks, statistical software packages, and is the research question? My intention here is to introduce the main elements of data analysis and provide a place for you to start when planning this part of your study. Knowledge gained from descriptive statistics helps investigators learn more about the study sample. It is used to analyze documented information in the form of texts, media, or even physical items.
Here are 4 methods to deal with missing data. Deriving absolute meaning from such data is nearly impossible; hence, it is mostly used for exploratory research.
Data analysis is how researchers go from a mass of data to meaningful insights. In addition to pie graphs, bar graphs, and histograms, many other types of figures are available for the visual representation of data. Mode: most common value among a set of values. The purpose of this article is to help you create a data analysis plan for a quantitative study.
Developing a framework: Also known as coding or indexing, here the researcher identifies broad ideas, concepts, behaviors, or phrases and assigns codes to them.