Using Excel in Research Hui Bian Office for Faculty Excellence
Data entry in Excel Directly type information into the cells Enter data using Form Command: File > Options 2
Data entry in Excel Tool bar: under Home 3
Data entry in Excel Enter data use Form Data Click New to add the second record You need to enter at least one data value, then select the entire range including variable names. Click Form to get 4
Data entry in Excel Enter a row of column headings that provide labels for the variables (recommend the first row). This form gives you the option of searching for and editing data entries as well as adding and deleting entries. 5
Data entry in Excel Use building-in functions to do calculation AutoSum: for example we want to know the average scores for V1 and V2. Create a new variable: average Select the empty cells below two variables Click Autosum 6
Data entry in Excel From the Drop Down menu, select Average 7
Data entry in Excel Data cleaning For example, data files from online survey, multiple responses to one question. This question has 8 response options 8
Data entry in Excel First create 8 variables based on the number of response options for the question 9
Data entry in Excel Data cleaning: separate those responses. Highlight the column(not include the variable) Click Data > Text to Columns 10
Data entry in Excel Data cleaning 11
Data entry in Excel Data cleaning Indicate where to store the data 12
Data entry in Excel Data cleaning 13
Creating a table Creating a table from an empty range First, select a range that is the location for the table. Choose Insert > table 14
Creating a table 15
Creating a table New table: double click headers, you can change their names 16
Creating a table Creating a table from a range of data Highlight the range of data Under Home stab, click Format as table Or Choose Insert > Table under Tables group Make sure the range contains your data. Or use Ctrl + T 17
Creating a table 18
Creating a table Format a table 19
Creating a table Tables allow us easily to get summary statistics: they are shown for the selected data in the Status bar at the bottom of the workbook. 20
Creating a table Table tools Add a Total Row: under Design, check the box for Total Row 21
Creating a table Table tools Use Total row function to do some calculations 22
Creating a table Use filters: Example: we want to know the average days of exercise among drinkers. We use two variables: Alcohol and Exercise Alcohol = 1 means non-drinkers. So we want to exclude them from the analysis. 23
Creating a table Go to variable Alcohol Click here Uncheck 24
Creating a table Get summary statistics among drinkers 25
Creating a table There is another way to use filters 26
Creating a table Table tools Remove duplicates: Example: we want to remove duplicates for variable height First make a copy of original data Then, remove duplicates from the new data sheet. 27
Creating a table Make a copy for the original data: you also can give a new name to the copy. Right click 28
Creating a table Table tools Remove duplicates: Table Tools tab > Design > Remove Duplicates 29
Creating a table After remove duplicates 30
Delete tables Delete a table without losing the data or table formatting Click anywhere in the table. On the Design tab, in the Tools group, click Convert to Range, and then click Yes. Table features are no longer available after you convert the table back to a range. For example, the row headers no longer include the sort and filter arrows. 31
Pivot tables and charts The Pivot tables are used to summarize, analyze, explore and present your data. A Pivot table is a way to extract data from a long list of information and present it in a more meaningful format. 32
Pivot tables and charts Use sample data from Excel 2007 (Walkenbach, 2007) File name: pivot table Creating a pivot table Insert > Pivot table under Tables group Or change your data into a table first, then click Summarize with Pivot Table 33
Pivot tables and charts 34
Pivot tables and charts What kind pivot table you need depends on what kind questions you want to answer. Example1 We want to know the summary of total amounts by branch (two-way contingency table). 35
Pivot tables and charts You also can drag the fields to the left window 36
Pivot tables and charts Here is the contingency table 37
Pivot tables and charts Change the fields settings Click 38
Pivot tables and charts Calculations: Click Show Values As to get new values for Amount. 39
Pivot tables and charts Example 2: we want to know the summary of total amounts by branch and by account types. Drag Acctype under Column labels 40
Pivot tables and charts Format Pivot tables Under PivotTable Tools, choose PivotTable options > click Display > check the box for Classic PivotTable Layout 41
Pivot tables and charts The pivot table is like this: 42
Pivot tables and charts We can drag filter field into the pivot table Field OpenedBy is the filter field. Just drag it to the Drop Report Filter area 43
Pivot tables and charts Format pivot table Select any cell in the pivot table Click Design under PivotTable tools You can play with different functions under Design 44
Pivot tables and charts Example3: We want to add OpenedBy as another row label. 45
Pivot tables and charts Copy of Pivot table Select the entire table > choose Home > Copy Paste to another Sheet or to Word 46
Pivot table and charts More examples Example 4: What is the daily total deposit amount for each branch? We need three fields: Branch, Date, and Amount Date is under Row Label and Brach is under Column Labels, 47
Pivot tables and charts Example 5: how many accounts were opened at each branch, broken down by account type. We need three fields: AcctType, Branch, Amount Change the values to Count 48
Pivot tables and charts Example 6: What types accounts do tellers open most often? Three fields are needed: AcctType, Openedby, and Amount AcctType is in the Row Labels, OpenedBy is in the Report Filters, and Amount is in the Values. 49
Pivot tables and charts Example 6 Filter 50
Pivot tables and charts Example 7: how does the Central branch compare to the other two branches? AcctType field is in the Row Label Branch field is in the Column Label Amount field is in the Values section We need to group North county and Westside together. 51
Pivot tables and charts Highlight North County and Westside columns Click Group Selections under Options tab Remove Branch from Column Labels Group1 includes North County and Westside branches. We can change name Group1 to Other Branches by typing name inside formula bar. 52
Pivot tables and charts Change names for column fields Type a new name 53
Pivot tables and charts Pivot Charts: we want column chart for example 7. Select any cell inside pivot table Choose PivotTable Tools > Options > Tools > Pivot Chart > Choose Column. We can change the Axis fields by dragging fields around 54
Pivot tables and charts Format charts: PivotChart Tools 55
Pivot tables and charts Add title for the chart Click Layout > Chart Title > Above Chart Type a name 56
Pivot tables and charts Show data labels Click Layout > Data Labels > Show 57
Pivot tables and charts Example 8: What s the dollar distribution of the different account types? Only one field is needed: Amount (under Row Label) Group Amount variable by using 5000 as an interval. 58
Pivot tables and charts How to group Amount variable Highlight Field name 59
Pivot tables and charts Dollar distribution for each type of account Drag AccType field under Filter Drag Amount field under Values Change Amount values: % of grand total We can change field names by double clicking it or change it directly inside formula bar. 60
Limitations of Pivot table You cannot add new rows or columns You cannot change any of the calculated values You cannot enter formulas within the pivot table. 61
Analyzing data with the Analysis Toolpak Install Toolpak add-in File > Options > double click Add-ins > Highlight Analysis Toolpak > Click Go 62
Analyzing data with the Analysis Toolpak 63
Analyzing data with the Analysis Toolpak Click Ok 64
Analyzing data with the Analysis Toolpak Go to Data > Analysis 65
Analyzing data with the Analysis Toolpak Analysis tools 66
Analyzing data with the Analysis Toolpak Descriptive Statistics Example: descriptive statistics for four variables: height, weight, alcohol, and exercise Highlight four columns including headers. 67
Analyzing data with the Analysis Toolpak Outputs 68
Analyzing data with the Analysis Toolpak Analysis of variances (ANOVA) ANOVA single factor: a one-way analysis of variance with two or more groups. Example: we have three groups (group 1, group 2, and group 3). We want to know whether there is a difference in knowledge test scores among three groups 69
Analyzing data with the Analysis Toolpak Analysis of variances (ANOVA) ANOVA single factor (data set) 70
Analyzing data with the Analysis Toolpak ANOVA single factor In "Input Range" highlight the entire range of data. 71
Analyzing data with the Analysis Toolpak ANOVA: Single Factor: output 72
Analyzing data with the Analysis toolpak ANOVA single factor: interpret results The mean level of knowledge score reported by group 1 is 39.25, by group 2 is 46.00, and by group 3 is 41.35. According to the test result F (2, 57) = 3.47, p =.04, With an alpha level of.05, the critical F = 3.15. Therefore, since the F statistic is larger than the critical value, we reject the null hypothesis. 73
Analyzing data with the Analysis toolpak ANOVA: Two-Factor with Replication Example: we have two factors: gender and two different drugs. We want to know whether there is different drug effects by gender. 74
Analyzing data with the Analysis toolpak ANOVA: Two-Factor with Replication Data layout 75
Analyzing data with the Analysis toolpak ANOVA: Two-Factor with Replication 76
Analyzing data with the Analysis Toolpak ANOVA results Sample: drug Columns: gender Interaction: drug by gender 77
Analyzing data with the Analysis Toolpak ANOVA: Two-Factor with Replication Results interpretation: the P-value for Male/Female is greater than alpha (.47 >.05), so we accept the null hypothesis that the means are the same across gender groups. The P-Value for Drugs is greater than alpha (.00 <.05), so the null hypothesis is rejected and there is a different drug effect. The P-value for the interaction of the drugs and gender is greater than alpha (.67 <.05), so we accept the null hypothesis. 78
Analyzing data with the Analysis Toolpak Correlation Example: we want to measure the degree to which group1, group2 and group3 are correlated. 79
Analyzing data with the Analysis Correlation toolpak 80
Analyzing data with the Analysis Toolpak Regression: Example: we use a data file from Excel 2007 Bible (Walkenbach, 2007): variable Deposits is predicted by variables Adv and bp diff. 81
Analyzing data with the Analysis Toolpak Regression data 82
Analyzing data with the Analysis Regression Toolpak 83
Analyzing data with the Analysis Toolpak Regression results 84
Analyzing data with the Analysis Toolpak Regression results 85
Analyzing data with the Analysis Toolpak Regression: plots 86
Analyzing data with the Analysis Toolpak T test Paired two-sample for means Example: is there any difference between time 1 and time 2 (use drug variables). 87
Analyzing data with the Analysis Toolpak T test results 88
Analyzing data with the Analysis Toolpak Random numbers 89
Analyzing data with the Analysis Toolpak Results 90
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